Collate CPUE data
Max Lindmark
16 March, 2023
Intro
In this script, I load exchange data from datras and calculate catch of cod and flounder in unit kg/km^2 (with TVL gear), by correcting for gear dimensions, sweeplength and trawl speed, following Orio et al 2017. I also add oxygen, temperature and depth covariates, which I use for modelling this biomass index. I do this for Q1 and Q4. I use Q4 for the condition data. For the condition data, I also need the environmental covariates. Hence, the study is limited to 2019. For other analyses, I need Q1, and don’t need all envi. covariates, and I have stomach data incl. 2020, hence I need to standardize all trawl data available from Datras.
Load libraries
library(tidyverse)
#> Warning: package 'tidyr' was built under R version 4.0.5
library(readxl)
library(tidylog)
library(RCurl)
library(viridis)
library(RColorBrewer)
#> Warning: package 'RColorBrewer' was built under R version 4.0.5
library(patchwork)
library(janitor)
library(icesDatras)
library(mapdata)
library(patchwork)
library(rgdal)
library(raster)
library(sf)
#> Warning: package 'sf' was built under R version 4.0.5
library(rgeos)
library(chron)
library(lattice)
library(ncdf4)
library(marmap)
library(rnaturalearth)
library(rnaturalearthdata)
library(mapplots)
library(geosphere)
#remotes::install_github("pbs-assess/sdmTMB")
library(sdmTMB)
#library(gganimate)
world <- ne_countries(scale = "medium", returnclass = "sf")For maps
# Specify map ranges
ymin = 54; ymax = 58; xmin = 12; xmax = 22
map_data <- rnaturalearth::ne_countries(
scale = "medium",
returnclass = "sf", continent = "europe")
# Crop the polygon for plotting and efficiency:
# st_bbox(map_data) # find the rough coordinates
sf::sf_use_s2(FALSE)
swe_coast <- suppressWarnings(suppressMessages(
st_crop(map_data,
c(xmin = xmin, ymin = ymin, xmax = xmax, ymax = ymax))))
# Transform our map into UTM 33 coordinates, which is the equal-area projection we fit in:
utm_zone33 <- 32633
swe_coast_proj <- sf::st_transform(swe_coast, crs = utm_zone33)
#ggplot(swe_coast_proj) + geom_sf()
# Define plotting theme for main plot
theme_plot <- function(base_size = 10, base_family = "") {
theme_light(base_size = 10, base_family = "") +
theme(
axis.text.x = element_text(angle = 90),
axis.text = element_text(size = 8),
legend.text = element_text(size = 8),
legend.title = element_text(size = 8),
legend.position = "bottom",
legend.key.height = unit(0.2, "cm"),
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(-5, -5, -5, -5),
strip.text = element_text(size = 8, colour = 'gray10', margin = margin(b = 1, t = 1)),
strip.background = element_rect(fill = "grey95")
)
}Read data
# Data were read in from getDATRAS on 2021.10.13
# Read HH data
# bits_hh <- getDATRAS(record = "HH", survey = "BITS", years = 1991:2020, quarters = c(1, 4))
# write.csv(bits_hh, "data/DATRAS_exchange/bits_hh.csv")
bits_hh <- read.csv("data/DATRAS_exchange/bits_hh.csv")
# Read HL data
# bits_hl <- getDATRAS(record = "HL", survey = "BITS", years = 1991:2020, quarters = c(1, 4))
# write.csv(bits_hl, "data/DATRAS_exchange/bits_hl.csv")
bits_hl <- read.csv("data/DATRAS_exchange/bits_hl.csv")
# Read CA data
# bits_ca <- getDATRAS(record = "CA", survey = "BITS", years = 1991:2020, quarters = c(1, 4))
# write.csv(bits_ca, "data/DATRAS_exchange/bits_ca.csv")
bits_ca <- read.csv("data/DATRAS_exchange/bits_ca.csv")
# Read gear standardization data
#sweep <- read.csv("data/from_ale/sweep_9116.csv", sep = ";", dec = ",", fileEncoding = "latin1")
sweep <- read.csv("data/from_ale/sweep_9118_ml.csv", sep = ";", fileEncoding = "latin1")Standardize catch data
Standardize ships
# Before creating a a new ID, make sure that countries and ships names use the same format
sort(unique(sweep$Ship))
#> [1] "26HF" "ATL" "ATLD" "BAL" "BALL" "BPE" "CEV" "CLP" "CLV" "COML"
#> [11] "DAN2" "DANS" "DAR" "GDY" "HAF" "KOH" "KOOT" "MON" "MONL" "SOL"
#> [21] "SOL2" "VSH" "ZBA"
sort(unique(bits_hh$Ship))
#> [1] "06JR" "06S1" "06SL" "26D4" "26HF" "26HI" "67BC" "77AR" "77MA" "77SE"
#> [11] "90MX" "AA36" "ESLF" "ESOR" "ESTM" "LAIZ" "LTDA" "RUEK" "RUJB" "RUNT"
#> [21] "RUS6"
sort(unique(bits_hl$Ship))
#> [1] "06JR" "06S1" "06SL" "26D4" "26HF" "26HI" "67BC" "77AR" "77MA" "77SE"
#> [11] "90MX" "AA36" "ESLF" "ESOR" "ESTM" "LAIZ" "LTDA" "RUEK" "RUJB" "RUNT"
#> [21] "RUS6"
# Change back to the old Ship name standard...
# https://vocab.ices.dk/?ref=315
# https://vocab.ices.dk/?ref=315
# Assumptions:
# SOL is Solea on ICES links above, and SOL1 is the older one of the two SOLs (1 and 2)
# DAN is Dana
# sweep %>% filter(Ship == "DANS") %>% distinct(Year, Country)
# sweep %>% filter(Ship == "DAN2") %>% distinct(Year)
# bits_hh %>% filter(Ship == "67BC") %>% distinct(Year, Country)
# sweep %>% filter(Ship == "DAN2") %>% distinct(Year)
# bits_hh %>% filter(Ship == "26D4") %>% distinct(Year) # Strange that 26DF doesn't extend far back. Which ship did the Danes use? Ok, I have no Danish data that old.
# bits_hh %>% filter(Country == "DK") %>% distinct(Year)
bits_hh <- bits_hh %>%
mutate(Ship2 = fct_recode(Ship,
"SOL" = "06S1",
"SOL2" = "06SL",
"DAN2" = "26D4",
"HAF" = "26HF",
"HAF" = "26HI",
"HAF" = "67BC",
"BAL" = "67BC",
"ARG" = "77AR",
"77SE" = "77SE",
"AA36" = "AA36",
"KOOT" = "ESLF",
"KOH" = "ESTM",
"DAR" = "LTDA",
"ATLD" = "RUJB",
"ATL" = "RUNT"),
Ship2 = as.character(Ship2)) %>%
mutate(Ship3 = ifelse(Country == "LV" & Ship2 == "BAL", "BALL", Ship2))
bits_hl <- bits_hl %>%
mutate(Ship2 = fct_recode(Ship,
"SOL" = "06S1",
"SOL2" = "06SL",
"DAN2" = "26D4",
"HAF" = "26HF",
"HAF" = "26HI",
"HAF" = "67BC",
"BAL" = "67BC",
"ARG" = "77AR",
"77SE" = "77SE",
"AA36" = "AA36",
"KOOT" = "ESLF",
"KOH" = "ESTM",
"DAR" = "LTDA",
"ATLD" = "RUJB",
"ATL" = "RUNT"),
Ship2 = as.character(Ship2)) %>%
mutate(Ship3 = ifelse(Country == "LV" & Ship2 == "BAL", "BALL", Ship2))
bits_ca <- bits_ca %>%
mutate(Ship2 = fct_recode(Ship,
"SOL" = "06S1",
"SOL2" = "06SL",
"DAN2" = "26D4",
"HAF" = "26HF",
"HAF" = "26HI",
"HAF" = "67BC",
"BAL" = "67BC",
"ARG" = "77AR",
"77SE" = "77SE",
"AA36" = "AA36",
"KOOT" = "ESLF",
"KOH" = "ESTM",
"DAR" = "LTDA",
"ATLD" = "RUJB",
"ATL" = "RUNT"),
Ship2 = as.character(Ship2)) %>%
mutate(Ship3 = ifelse(Country == "LV" & Ship2 == "BAL", "BALL", Ship2))
# Ok, which ships are missing in the exchange data?
unique(bits_hh$Ship3)[!unique(bits_hh$Ship3) %in% unique(sweep$Ship)]
#> [1] "AA36" "90MX" "ARG" "06JR" "LAIZ" "RUEK" "RUS6" "77MA" "77SE" "ESOR"
# Swedish Ships and unidentified ships are NOT in the Sweep data
unique(sweep$Ship3)[!unique(sweep$Ship3) %in% unique(bits_hh$Ship3)]
#> NULL
# But all Sweep Ships are in the exchange dataStandardize countries
# Now check which country codes are used
sort(unique(sweep$Country))
#> [1] "DEN" "EST" "GFR" "LAT" "LTU" "POL" "RUS" "SWE"
sort(unique(bits_hh$Country))
#> [1] "DE" "DK" "EE" "LT" "LV" "PL" "RU" "SE"
# https://www.nationsonline.org/oneworld/country_code_list.htm#E
bits_hh <- bits_hh %>%
mutate(Country = fct_recode(Country,
"DEN" = "DK",
"EST" = "EE",
"GFR" = "DE",
"LAT" = "LV",
"LTU" = "LT",
"POL" = "PL",
"RUS" = "RU",
"SWE" = "SE"),
Country = as.character(Country))
bits_hl <- bits_hl %>%
mutate(Country = fct_recode(Country,
"DEN" = "DK",
"EST" = "EE",
"GFR" = "DE",
"LAT" = "LV",
"LTU" = "LT",
"POL" = "PL",
"RUS" = "RU",
"SWE" = "SE"),
Country = as.character(Country))
bits_ca <- bits_ca %>%
mutate(Country = fct_recode(Country,
"DEN" = "DK",
"EST" = "EE",
"GFR" = "DE",
"LAT" = "LV",
"LTU" = "LT",
"POL" = "PL",
"RUS" = "RU",
"SWE" = "SE"),
Country = as.character(Country))
# Gear? Are they the same?
sort(unique(bits_hh$Gear))
#> [1] "CAM" "CHP" "DT" "EGY" "ESB" "EXP" "FOT" "GOV" "GRT" "H20" "HAK" "LBT"
#> [13] "P20" "PEL" "SON" "TVL" "TVS"
sort(unique(bits_hl$Gear))
#> [1] "CAM" "CHP" "DT" "EGY" "ESB" "EXP" "FOT" "GOV" "GRT" "H20" "HAK" "LBT"
#> [13] "P20" "PEL" "SON" "TVL" "TVS"
sort(unique(sweep$Gear))
#> [1] "CAM" "CHP" "DT" "EGY" "ESB" "EXP" "GRT" "H20" "HAK" "LBT" "LPT" "P20"
#> [13] "PEL" "SON" "TVL" "TVS"
# Which gears are NOT in the sweep data?
unique(bits_hl$Gear)[!unique(bits_hl$Gear) %in% unique(sweep$Gear)]
#> [1] "FOT" "GOV"Create a simple haul ID that works across all exchange data
# Create ID column
bits_ca <- bits_ca %>%
mutate(IDx = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
bits_hl <- bits_hl %>%
mutate(IDx = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
bits_hh <- bits_hh %>%
mutate(IDx = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
# Works like a haul-id
bits_hh %>% group_by(IDx) %>% mutate(n = n()) %>% ungroup() %>% distinct(n)
#> # A tibble: 1 × 1
#> n
#> <int>
#> 1 1Create the same unique haul-ID in the cpue data that I have in the sweep-file
bits_hl <- bits_hl %>%
mutate(haul.id = paste(Year, Quarter, Country, Ship3, Gear, StNo, HaulNo, sep = ":"))
bits_hh <- bits_hh %>%
mutate(haul.id = paste(Year, Quarter, Country, Ship3, Gear, StNo, HaulNo, sep = ":"))
bits_hh %>% group_by(haul.id) %>% mutate(n = n()) %>% ungroup() %>% distinct(n)
#> # A tibble: 1 × 1
#> n
#> <int>
#> 1 1Clean DATRAS EXCHANGE data
# Select just valid, additional and no oxygen hauls
bits_hh <- bits_hh %>%
#filter(!Country == "SWE") %>% # I'll deal with Sweden later...
filter(HaulVal %in% c("A","N","V"))
# Add ICES rectangle
bits_hh$Rect <- mapplots::ices.rect2(lon = bits_hh$ShootLong, lat = bits_hh$ShootLat)
# Add ICES subdivisions
shape <- shapefile("data/ICES_StatRec_mapto_ICES_Areas/StatRec_map_Areas_Full_20170124.shp")
pts <- SpatialPoints(cbind(bits_hh$ShootLong, bits_hh$ShootLat),
proj4string = CRS(proj4string(shape)))
#> Warning in proj4string(shape): CRS object has comment, which is lost in output
bits_hh$sub_div <- over(pts, shape)$Area_27
# Rename subdivisions to the more common names and do some more filtering (by sub div and area)
sort(unique(bits_hh$sub_div))
#> [1] "3.a.20" "3.a.21" "3.b.23" "3.c.22" "3.d.24" "3.d.25"
#> [7] "3.d.26" "3.d.27" "3.d.28.1" "3.d.28.2" "3.d.29"
bits_hh <- bits_hh %>%
mutate(sub_div = factor(sub_div),
sub_div = fct_recode(sub_div,
"20" = "3.a.20",
"21" = "3.a.21",
"22" = "3.c.22",
"23" = "3.b.23",
"24" = "3.d.24",
"25" = "3.d.25",
"26" = "3.d.26",
"27" = "3.d.27",
"28" = "3.d.28.1",
"28" = "3.d.28.2",
"29" = "3.d.29"),
sub_div = as.character(sub_div))
# Now add the fishing line information from the sweep file (we need that later
# to standardize based on gear geometry). We add in the the HH data and then
# transfer it to the other exchange data files when left_joining.
# Check which Fishing lines I have in the sweep data:
fishing_line <- sweep %>% group_by(Gear) %>% distinct(Fishing.line)
bits_hh <- left_join(bits_hh, fishing_line)
# sweep %>% group_by(Gear) %>% distinct(Fishing.line)
# bits_hh %>% group_by(Gear) %>% distinct(Fishing.line)
bits_hh$Fishing.line <- as.numeric(bits_hh$Fishing.line)
# Which gears do now have fishing line?
bits_hh$Fishing.line[is.na(bits_hh$Fishing.line)] <- -9
bits_hh %>% filter(Fishing.line == -9) %>% distinct(Gear)
#> Gear
#> 1 GRT
#> 2 FOT
#> 3 GOV
#> 4 EXP
#> 5 CAM
#> 6 EGY
#> 7 DT
#> 8 ESB
#> 9 HAK
# 1 GRT
# 2 CAM
# 3 EXP
# 4 FOT
# 5 GOV
# 6 EGY
# 7 DT
# 8 ESB
# 9 HAK
# FROM the index files (Orio, "Research Östersjön 2")
# FOT has 83
# GOV has 160
# ESB ??
# GRT ??
# Rest are unknown and likely not used by Swedish data (therefore their correction
# factors my be in the sweep file)
# Add these values:
bits_hh <- bits_hh %>% mutate(Fishing.line = ifelse(Gear == "FOT", 83, Fishing.line))
bits_hh <- bits_hh %>% mutate(Fishing.line = ifelse(Gear == "GOV", 160, Fishing.line))
# Now select the hauls in the HH data when subsetting the HL data
bits_hl <- bits_hl %>%
filter(haul.id %in% bits_hh$haul.id)
# Match columns from the HH data to the HL and CA data
sort(unique(bits_hh$sub_div))
#> [1] "20" "21" "22" "23" "24" "25" "26" "27" "28" "29"
sort(colnames(bits_hh))
#> [1] "BotCurDir" "BotCurSpeed" "BotSal"
#> [4] "BotTemp" "Buoyancy" "BySpecRecCode"
#> [7] "CodendMesh" "Country" "DataType"
#> [10] "DateofCalculation" "Day" "DayNight"
#> [13] "Depth" "DepthStratum" "Distance"
#> [16] "DoorSpread" "DoorSurface" "DoorType"
#> [19] "DoorWgt" "Fishing.line" "Gear"
#> [22] "GearEx" "GroundSpeed" "haul.id"
#> [25] "HaulDur" "HaulLat" "HaulLong"
#> [28] "HaulNo" "HaulVal" "HydroStNo"
#> [31] "IDx" "KiteDim" "MaxTrawlDepth"
#> [34] "MinTrawlDepth" "Month" "Netopening"
#> [37] "PelSampType" "Quarter" "RecordType"
#> [40] "Rect" "Rigging" "SecchiDepth"
#> [43] "Ship" "Ship2" "Ship3"
#> [46] "ShootLat" "ShootLong" "SpeedWater"
#> [49] "StatRec" "StdSpecRecCode" "StNo"
#> [52] "sub_div" "SurCurDir" "SurCurSpeed"
#> [55] "SurSal" "SurTemp" "Survey"
#> [58] "SweepLngt" "SwellDir" "SwellHeight"
#> [61] "ThClineDepth" "ThermoCline" "Tickler"
#> [64] "TidePhase" "TideSpeed" "TimeShot"
#> [67] "TowDir" "Turbidity" "WarpDen"
#> [70] "Warpdia" "Warplngt" "WgtGroundRope"
#> [73] "WindDir" "WindSpeed" "WingSpread"
#> [76] "X" "Year"
bits_hh_merge <- bits_hh %>%
dplyr::select(sub_div, Rect, HaulVal, StdSpecRecCode, BySpecRecCode, Fishing.line,
DataType, HaulDur, GroundSpeed, haul.id, IDx, ShootLat, ShootLong, Depth)
bits_hl <- left_join(dplyr::select(bits_hl, -haul.id), bits_hh_merge, by = "IDx")
bits_ca <- left_join(bits_ca, bits_hh_merge, by = "IDx")
# Now filter the subdivisions I want from all data sets
bits_hh <- bits_hh %>% filter(sub_div %in% c(24, 25, 26, 27, 28))
bits_hl <- bits_hl %>% filter(sub_div %in% c(24, 25, 26, 27, 28))
bits_ca <- bits_ca %>% filter(sub_div %in% c(24, 25, 26, 27, 28))Filter species
hlcod <- bits_hl %>%
filter(SpecCode %in% c("126436", "164712")) %>%
mutate(Species = "Gadus morhua")
hlfle <- bits_hl %>%
filter(SpecCode %in% c("127141", "172894")) %>%
mutate(Species = "Platichthys flesus")Prepare to add 0 catches
# Find common columns in the HH and HL data (here already subset by species)
comcol <- intersect(names(hlcod), names(bits_hh))
# What is the proportion of zero-catch hauls?
# Here we don't have zero catches
hlcod %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(HLNoAtLngt)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
distinct(zero_catch)
#> # A tibble: 1 × 1
#> zero_catch
#> <chr>
#> 1 N
# Cod: Add 0s and then remove lines with SpecVal = 0 (first NA because we don't have a match in the HH, then make them 0 later)
hlcod0 <- full_join(hlcod, bits_hh[, comcol], by = comcol)
# No zeroes yet
hlcod0 %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(HLNoAtLngt)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
distinct(zero_catch)
#> # A tibble: 1 × 1
#> zero_catch
#> <lgl>
#> 1 NA
hlcod0$SpecVal[is.na(hlcod0$SpecVal)] <- "zeroCatch"
hlcod0$SpecVal <- factor(hlcod0$SpecVal)
hlcod0 <- hlcod0 %>% filter(!SpecVal == "0")
# Add species again after merge
hlcod0$Species<-"Gadus morhua"
# Flounder: Add 0s, remove them if StdSpecRecCode !=1 and then remove lines with SpecVal = 0
hlfle0 <- full_join(hlfle, bits_hh[, comcol], by = comcol)
hlfle0 <- hlfle0[!(is.na(hlfle0$Species) & hlfle0$StdSpecRecCode != 1),]
hlfle0$SpecVal[is.na(hlfle0$SpecVal)] <- "zeroCatch"
hlfle0$SpecVal <- factor(hlfle0$SpecVal)
hlfle0 <- hlfle0 %>% filter(!SpecVal == "0")
hlfle0$Species<-"Platichthys flesus"
# Check number of hauls per species
hlcod0 %>% distinct(haul.id) %>% nrow()
#> [1] 12488
hlfle0 %>% distinct(haul.id) %>% nrow()
#> [1] 12214Create (unstandardized) CPUE for SpecVal=1. If
DataType=C then CPUEun=HLNoAtLngt, if
DataType=R then
CPUEun=HLNoAtLngt/(HaulDur/60), if DataType=S
then CPUEun=(HLNoAtLngt*SubFactor)/(HaulDur/60). If
SpecVal="zeroCatch" then CPUEun=0, if
SpecVal=4 we need to decide (no length measurements, only
total catch). Note that here we also add zero CPUE if
SpecVal=="zeroCatch".
Then I will sum for the same haul the CPUE of the same length classes if they were sampled with different subfactors or with different sexes.
# Cod
hlcod0 <- hlcod0 %>%
mutate(CPUEun = ifelse(SpecVal == "1" & DataType == "C",
HLNoAtLngt,
ifelse(SpecVal == "1" & DataType == "R",
HLNoAtLngt/(HaulDur/60),
ifelse(SpecVal == "1" & DataType == "S",
(HLNoAtLngt*SubFactor)/(HaulDur/60),
ifelse(SpecVal == "zeroCatch", 0, NA)))))
# Plot and fill by zero catch
hlcod0 %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
group_by(Year, zero_catch) %>%
summarise(n = n()) %>%
ggplot(., aes(x = Year, y = n, fill = zero_catch)) +
geom_bar(stat = "identity")
# Some rows have multiple rows per combination of length class and haul id,
# so we need to sum it up
hlcod0 %>% group_by(LngtClass, haul.id) %>% mutate(n = n()) %>% ungroup() %>% distinct(n)
#> # A tibble: 2 × 1
#> n
#> <int>
#> 1 1
#> 2 2
hlcod0 %>% group_by(LngtClass, haul.id) %>% mutate(n = n()) %>% ungroup() %>% filter(n == 2) %>% as.data.frame() %>% head(20)
#> X RecordType Survey Quarter Country Ship Gear SweepLngt GearEx DoorType
#> 1 6847 HL BITS 1 LAT 90MX LBT NA <NA> NA
#> 2 13793 HL BITS 1 LAT 90MX LBT NA <NA> NA
#> 3 13988 HL BITS 1 LAT 90MX LBT NA <NA> NA
#> 4 58045 HL BITS 1 LAT LAIZ LBT NA <NA> NA
#> 5 205495 HL BITS 1 RUS RUJB HAK NA <NA> NA
#> 6 300422 HL BITS 4 EST ESLF TVS NA <NA> NA
#> 7 300423 HL BITS 4 EST ESLF TVS NA <NA> NA
#> 8 325369 HL BITS 4 DEN 26D4 TVL NA R NA
#> 9 325375 HL BITS 4 DEN 26D4 TVL NA R NA
#> 10 326062 HL BITS 4 DEN 26D4 TVL NA R NA
#> 11 326064 HL BITS 4 DEN 26D4 TVL NA R NA
#> 12 326065 HL BITS 4 DEN 26D4 TVL NA R NA
#> 13 326066 HL BITS 4 DEN 26D4 TVL NA R NA
#> 14 326067 HL BITS 4 DEN 26D4 TVL NA R NA
#> 15 326068 HL BITS 4 DEN 26D4 TVL NA R NA
#> 16 326069 HL BITS 4 DEN 26D4 TVL NA R NA
#> 17 326071 HL BITS 4 DEN 26D4 TVL NA R NA
#> 18 326073 HL BITS 4 DEN 26D4 TVL NA R NA
#> 19 326077 HL BITS 4 DEN 26D4 TVL NA R NA
#> 20 326079 HL BITS 4 DEN 26D4 TVL NA R NA
#> StNo HaulNo Year SpecCodeType SpecCode SpecVal Sex TotalNo CatIdentifier
#> 1 000001 5 1991 W 126436 4 <NA> 3 1
#> 2 000003 33 1991 W 126436 4 <NA> 6 1
#> 3 000003 29 1991 W 126436 4 <NA> 183 1
#> 4 000001 23 1993 W 126436 4 <NA> 1 1
#> 5 <NA> 10 1998 T 164712 4 <NA> -9 1
#> 6 14 14 2000 T 164712 1 M 2 1
#> 7 14 14 2000 T 164712 1 F 4 1
#> 8 63 31 2001 T 164712 1 M 5 1
#> 9 63 31 2001 T 164712 1 F 7 1
#> 10 83 37 2001 T 164712 1 M 21 1
#> 11 83 37 2001 T 164712 1 M 21 1
#> 12 83 37 2001 T 164712 1 M 21 1
#> 13 83 37 2001 T 164712 1 M 21 1
#> 14 83 37 2001 T 164712 1 M 21 1
#> 15 83 37 2001 T 164712 1 M 21 1
#> 16 83 37 2001 T 164712 1 M 21 1
#> 17 83 37 2001 T 164712 1 M 21 1
#> 18 83 37 2001 T 164712 1 M 21 1
#> 19 83 37 2001 T 164712 1 F 42 1
#> 20 83 37 2001 T 164712 1 F 42 1
#> NoMeas SubFactor SubWgt CatCatchWgt LngtCode LngtClass HLNoAtLngt DevStage
#> 1 NA 1 NA NA <NA> NA -9 <NA>
#> 2 NA 1 NA NA <NA> NA -9 <NA>
#> 3 NA 1 NA NA <NA> NA -9 <NA>
#> 4 NA 1 NA NA <NA> NA -9 <NA>
#> 5 NA 1 NA NA <NA> NA -9 <NA>
#> 6 3 1 NA 32 1 35 2 <NA>
#> 7 3 1 NA 32 1 35 2 <NA>
#> 8 5 1 NA 3142 1 22 2 <NA>
#> 9 7 1 NA 3142 1 22 1 <NA>
#> 10 21 1 NA 64261 1 22 1 <NA>
#> 11 21 1 NA 64261 1 38 2 <NA>
#> 12 21 1 NA 64261 1 39 2 <NA>
#> 13 21 1 NA 64261 1 41 1 <NA>
#> 14 21 1 NA 64261 1 42 1 <NA>
#> 15 21 1 NA 64261 1 44 2 <NA>
#> 16 21 1 NA 64261 1 45 4 <NA>
#> 17 21 1 NA 64261 1 48 2 <NA>
#> 18 21 1 NA 64261 1 52 1 <NA>
#> 19 42 1 NA 64261 1 22 1 <NA>
#> 20 42 1 NA 64261 1 38 2 <NA>
#> LenMeasType DateofCalculation Valid_Aphia Ship2 Ship3
#> 1 NA 20211203 126436 90MX 90MX
#> 2 NA 20211203 126436 90MX 90MX
#> 3 NA 20211203 126436 90MX 90MX
#> 4 NA 20211203 126436 LAIZ LAIZ
#> 5 NA 20140617 126436 ATLD ATLD
#> 6 NA 20131112 126436 KOOT KOOT
#> 7 NA 20131112 126436 KOOT KOOT
#> 8 NA 20131113 126436 DAN2 DAN2
#> 9 NA 20131113 126436 DAN2 DAN2
#> 10 NA 20131113 126436 DAN2 DAN2
#> 11 NA 20131113 126436 DAN2 DAN2
#> 12 NA 20131113 126436 DAN2 DAN2
#> 13 NA 20131113 126436 DAN2 DAN2
#> 14 NA 20131113 126436 DAN2 DAN2
#> 15 NA 20131113 126436 DAN2 DAN2
#> 16 NA 20131113 126436 DAN2 DAN2
#> 17 NA 20131113 126436 DAN2 DAN2
#> 18 NA 20131113 126436 DAN2 DAN2
#> 19 NA 20131113 126436 DAN2 DAN2
#> 20 NA 20131113 126436 DAN2 DAN2
#> IDx sub_div Rect HaulVal StdSpecRecCode
#> 1 1991.1.LAT.90MX.LBT.000001.5 28 43H1 V 1
#> 2 1991.1.LAT.90MX.LBT.000003.33 28 43H0 V 1
#> 3 1991.1.LAT.90MX.LBT.000003.29 28 43H0 V 1
#> 4 1993.1.LAT.LAIZ.LBT.000001.23 28 43H1 V 1
#> 5 1998.1.RUS.RUJB.HAK.NA.10 26 38G9 V 1
#> 6 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1
#> 7 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1
#> 8 2001.4.DEN.26D4.TVL.63.31 26 39G8 V 1
#> 9 2001.4.DEN.26D4.TVL.63.31 26 39G8 V 1
#> 10 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> 11 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> 12 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> 13 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> 14 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> 15 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> 16 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> 17 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> 18 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> 19 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> 20 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1
#> BySpecRecCode Fishing.line DataType HaulDur GroundSpeed
#> 1 1 28.00 C 40 -9.0
#> 2 1 28.00 C 30 -9.0
#> 3 1 28.00 C 20 -9.0
#> 4 1 28.00 C 60 -9.0
#> 5 1 -9.00 C 30 3.8
#> 6 1 33.22 C 30 3.0
#> 7 1 33.22 C 30 3.0
#> 8 1 63.46 R 30 3.0
#> 9 1 63.46 R 30 3.0
#> 10 1 63.46 R 31 3.1
#> 11 1 63.46 R 31 3.1
#> 12 1 63.46 R 31 3.1
#> 13 1 63.46 R 31 3.1
#> 14 1 63.46 R 31 3.1
#> 15 1 63.46 R 31 3.1
#> 16 1 63.46 R 31 3.1
#> 17 1 63.46 R 31 3.1
#> 18 1 63.46 R 31 3.1
#> 19 1 63.46 R 31 3.1
#> 20 1 63.46 R 31 3.1
#> haul.id ShootLat ShootLong Depth Species CPUEun
#> 1 1991:1:LAT:90MX:LBT:000001:5 57.3833 21.3000 35 Gadus morhua NA
#> 2 1991:1:LAT:90MX:LBT:000003:33 57.2167 20.7000 80 Gadus morhua NA
#> 3 1991:1:LAT:90MX:LBT:000003:29 57.1500 20.6500 93 Gadus morhua NA
#> 4 1993:1:LAT:LAIZ:LBT:000001:23 57.3000 21.2333 40 Gadus morhua NA
#> 5 1998:1:RUS:ATLD:HAK:NA:10 54.6333 19.6500 32 Gadus morhua NA
#> 6 2000:4:EST:KOOT:TVS:14:14 58.0167 21.0833 74 Gadus morhua 2.000000
#> 7 2000:4:EST:KOOT:TVS:14:14 58.0167 21.0833 74 Gadus morhua 2.000000
#> 8 2001:4:DEN:DAN2:TVL:63:31 55.4699 18.3116 88 Gadus morhua 4.000000
#> 9 2001:4:DEN:DAN2:TVL:63:31 55.4699 18.3116 88 Gadus morhua 2.000000
#> 10 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 1.935484
#> 11 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 3.870968
#> 12 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 3.870968
#> 13 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 1.935484
#> 14 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 1.935484
#> 15 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 3.870968
#> 16 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 7.741935
#> 17 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 3.870968
#> 18 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 1.935484
#> 19 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 1.935484
#> 20 2001:4:DEN:DAN2:TVL:83:37 55.6627 18.0414 69 Gadus morhua 3.870968
#> n
#> 1 2
#> 2 2
#> 3 2
#> 4 2
#> 5 2
#> 6 2
#> 7 2
#> 8 2
#> 9 2
#> 10 2
#> 11 2
#> 12 2
#> 13 2
#> 14 2
#> 15 2
#> 16 2
#> 17 2
#> 18 2
#> 19 2
#> 20 2
test <- hlcod0 %>% group_by(LngtClass, haul.id) %>% mutate(n = n()) %>% ungroup() %>% filter(n == 2)
test_id <- test$haul.id[2]
hlcodL <- hlcod0 %>%
group_by(LngtClass, haul.id) %>%
mutate(CPUEun = sum(CPUEun)) %>%
ungroup() %>%
mutate(id3 = paste(haul.id, LngtClass)) %>%
distinct(id3, .keep_all = TRUE) %>%
dplyr::select(-X, -id3) # Clean up a bit
# Check with an ID
# filter(hlcod0, haul.id == test_id)
# filter(hlcodL, haul.id == test_id) %>% as.data.frame()
# Do we still have 0 catches?
hlcodL %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
group_by(Year, zero_catch) %>%
summarise(n = n()) %>%
ggplot(., aes(x = Year, y = n, fill = zero_catch)) +
geom_bar(stat = "identity")
# Flounder
hlfle0 <- hlfle0 %>%
mutate(CPUEun = ifelse(SpecVal == "1" & DataType == "C",
HLNoAtLngt,
ifelse(SpecVal == "1" & DataType == "R",
HLNoAtLngt/(HaulDur/60),
ifelse(SpecVal == "1" & DataType == "S",
(HLNoAtLngt*SubFactor)/(HaulDur/60),
ifelse(SpecVal == "zeroCatch", 0, NA)))))
# Sum up the CPUES if multiple per length class and haul
hlfleL <- hlfle0 %>%
group_by(LngtClass, haul.id) %>%
mutate(CPUEun = sum(CPUEun)) %>%
ungroup() %>%
mutate(id3 = paste(haul.id, LngtClass)) %>%
distinct(id3, .keep_all = TRUE) %>%
dplyr::select(-X, -id3)Get and add annual weight-length relationships from the CA data for both cod and flounder so that I can calculate CPUE in biomass rather than numbers further down
# Cod
bits_ca_cod <- bits_ca %>%
filter(SpecCode %in% c("164712", "126436")) %>%
mutate(StNo = as.numeric(StNo)) %>%
mutate(Species = "Cod") %>%
mutate(ID = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
#> Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
# Now I need to copy rows with NoAtLngt > 1 so that 1 row = 1 ind
# First make a small test
# nrow(bits_ca_cod)
# test_id <- head(filter(bits_ca_cod, CANoAtLngt == 5))$ID[1]
# filter(bits_ca_cod, ID == test_id & CANoAtLngt == 5)
bits_ca_cod <- bits_ca_cod %>% map_df(., rep, .$CANoAtLngt)
# head(data.frame(filter(bits_ca_cod, ID == test_id & CANoAtLngt == 5)), 20)
# nrow(bits_ca_cod)
# Looks ok!
# Standardize length and drop NA weights (need that for condition)
bits_ca_cod <- bits_ca_cod %>%
drop_na(IndWgt) %>%
drop_na(LngtClass) %>%
filter(IndWgt > 0 & LngtClass > 0) %>% # Filter positive length and weight
mutate(weight_kg = IndWgt/1000) %>%
mutate(length_cm = ifelse(LngtCode == ".",
LngtClass/10,
LngtClass)) # Standardize length ((https://vocab.ices.dk/?ref=18))
# Plot
ggplot(bits_ca_cod, aes(IndWgt, length_cm)) +
geom_point() +
facet_wrap(~Year)
# Now extract the coefficients for each year (not bothering with outliers at the moment)
cod_intercept <- bits_ca_cod %>%
split(.$Year) %>%
purrr::map(~lm(log(IndWgt) ~ log(length_cm), data = .x)) %>%
purrr::map_df(broom::tidy, .id = 'Year') %>%
filter(term == "(Intercept)") %>%
mutate(a = exp(estimate)) %>%
mutate(Year = as.integer(Year)) %>%
dplyr::select(Year, a, std.error)
cod_slope <- bits_ca_cod %>%
split(.$Year) %>%
purrr::map(~lm(log(IndWgt) ~ log(length_cm), data = .x)) %>%
purrr::map_df(broom::tidy, .id = 'Year') %>%
filter(term == "log(length_cm)") %>%
mutate(Year = as.integer(Year)) %>%
rename("b" = "estimate") %>%
dplyr::select(Year, b, std.error)
# Flounder
bits_ca_fle <- bits_ca %>%
filter(SpecCode %in% c("127141", "172894")) %>%
mutate(StNo = as.numeric(StNo)) %>%
mutate(Species = "Flounder") %>%
mutate(ID = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
#> Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
bits_ca_fle <- bits_ca_fle %>% map_df(., rep, .$CANoAtLngt)
# Standardize length and drop NA weights (need that for condition)
bits_ca_fle <- bits_ca_fle %>%
drop_na(IndWgt) %>%
drop_na(LngtClass) %>%
filter(IndWgt > 0 & LngtClass > 0) %>% # Filter positive length and weight
mutate(weight_kg = IndWgt/1000) %>%
mutate(length_cm = ifelse(LngtCode == ".",
LngtClass/10,
LngtClass)) %>% # Standardize length ((https://vocab.ices.dk/?ref=18))
mutate(keep = ifelse(LngtCode == "." & Year == 2008, "N", "Y")) %>%
filter(keep == "Y") %>%
filter(length_cm < 70)
# Plot
ggplot(bits_ca_fle, aes(IndWgt, length_cm, color = LngtCode)) +
geom_point() +
facet_wrap(~Year)
# Now extract the coefficients for each year (not bothering with outliers at the moment)
fle_intercept <- bits_ca_fle %>%
split(.$Year) %>%
purrr::map(~lm(log(IndWgt) ~ log(length_cm), data = .x)) %>%
purrr::map_df(broom::tidy, .id = 'Year') %>%
filter(term == "(Intercept)") %>%
mutate(a = exp(estimate)) %>%
mutate(Year = as.integer(Year)) %>%
dplyr::select(Year, a, std.error)
fle_slope <- bits_ca_fle %>%
split(.$Year) %>%
purrr::map(~lm(log(IndWgt) ~ log(length_cm), data = .x)) %>%
purrr::map_df(broom::tidy, .id = 'Year') %>%
filter(term == "log(length_cm)") %>%
mutate(Year = as.integer(Year)) %>%
rename("b" = "estimate") %>%
dplyr::select(Year, b, std.error)
# Make a plot for the supporting information
#plot(cod_intercept$a ~ cod_slope$b)
pars <- bind_rows(cod_intercept %>% rename(est = a) %>% mutate(par = "a", Species = "Cod"),
cod_slope %>% rename(est = b) %>% mutate(par = "b", Species = "Cod"),
fle_intercept %>% rename(est = a) %>% mutate(par = "a", Species = "Flounder"),
fle_slope %>% rename(est = b) %>% mutate(par = "b", Species = "Flounder")) %>%
mutate(lwr = est - 1.96*std.error,
upr = est + 1.96*std.error) %>%
mutate(par = ifelse(par == "a", "Weight-length constant", "Weight-length exponent"))
coefs <- pars %>%
filter(Year > 1992) %>%
ggplot(aes(x = Year, y = est, color = Species)) +
geom_point() +
#geom_errorbar(width = 0) +
facet_wrap(~par, scales = "free") +
scale_color_brewer(palette = "Set2") +
coord_flip() +
labs(y = "Estimate") +
scale_x_continuous(breaks = c(1993:2020)) +
theme_plot() +
theme(axis.text.x = element_text(angle = 0))
dd_test <- data.frame(L = rep(seq(1, 30, 1), 2),
Year = as.factor(rep(c(1993, 2020), each = 30)),
a = rep(c(0.006, 0.015), each = 30),
b = rep(c(3.2, 2.9), each = 30)) %>%
mutate(W = a*L^b) %>%
ggplot(aes(L, W, color = Year)) +
scale_color_brewer(palette = "Set1") +
geom_line() +
theme_plot() +
theme(axis.text.x = element_text(angle = 0),
aspect.ratio = 3/4)
dd_test2 <- data.frame(L = rep(seq(5, 34, 1), 2),
Year = as.factor(rep(c(1993, 2020), each = 30)),
a = rep(c(0.006, 0.015), each = 30),
b = rep(c(3.2, 2.9), each = 30)) %>%
mutate(W = a*L^b) %>%
dplyr::select(-a, -b) %>%
pivot_wider(names_from = Year, values_from = W) %>%
mutate(ratio = `2020` / `1993`) %>%
ggplot(aes(L, ratio)) +
geom_line() +
theme_plot() +
geom_hline(yintercept = 1, linetype = 2, size = 0.5, color = "grey") +
labs(y = "Weight ratio 2020:1993") +
theme(axis.text.x = element_text(angle = 0),
aspect.ratio = 3/4)
coefs + (dd_test + dd_test2) + plot_annotation(tag_levels = "A") + plot_layout(heights = c(2, 1))
ggsave("figures/supp/density/annual_LW_pars.png", width = 4.5, height = 6.5, dpi = 600)
cod_intercept <- cod_intercept %>% dplyr::select(-std.error)
cod_slope <- cod_slope %>% dplyr::select(-std.error)
fle_intercept <- fle_intercept %>% dplyr::select(-std.error)
fle_slope <- fle_slope %>% dplyr::select(-std.error)Join the annual L-W relationships to the respective catch data to calculate CPUE in biomass not abundance
# These are the haul-data
# hlcodL
# hlfleL
hlcodL <- left_join(hlcodL, cod_intercept, by = "Year")
hlcodL <- left_join(hlcodL, cod_slope, by = "Year")
hlfleL <- left_join(hlfleL, fle_intercept, by = "Year")
hlfleL <- left_join(hlfleL, fle_slope, by = "Year")Convert from CPUE in numbers to kg
# First standardize length to cm and then check how zero-catches are implemented at this stage
hlcodL <- hlcodL %>%
mutate(length_cm = ifelse(LngtCode == ".",
LngtClass/10,
LngtClass)) # Standardize length ((https://vocab.ices.dk/?ref=18))
filter(hlcodL, length_cm == 0) # No such thing
#> # A tibble: 0 × 50
#> # … with 50 variables: RecordType <chr>, Survey <chr>, Quarter <int>,
#> # Country <chr>, Ship <chr>, Gear <chr>, SweepLngt <int>, GearEx <chr>,
#> # DoorType <lgl>, StNo <chr>, HaulNo <int>, Year <int>, SpecCodeType <chr>,
#> # SpecCode <int>, SpecVal <fct>, Sex <chr>, TotalNo <dbl>,
#> # CatIdentifier <int>, NoMeas <int>, SubFactor <dbl>, SubWgt <int>,
#> # CatCatchWgt <int>, LngtCode <chr>, LngtClass <int>, HLNoAtLngt <dbl>,
#> # DevStage <chr>, LenMeasType <int>, DateofCalculation <int>, …
# Now check if all rows where length is NA are the ones with zero catch!
hlcodL %>%
mutate(length2 = replace_na(length_cm, -9),
no_length = ifelse(length2 < 0, "T", "F")) %>%
ggplot(., aes(length2, CPUEun, color = no_length)) + geom_point(alpha = 0.2) + facet_wrap(~no_length)
#> Warning: Removed 5 rows containing missing values (geom_point).
# Right, so all hauls with zero catch have NA length_cm. I don't have any NA catches
t <- hlcodL %>% drop_na(CPUEun)
t <- hlcodL %>% filter(CPUEun == 0)
t <- hlcodL %>% drop_na(length_cm)
# In other words, a zero catch is when the catch is zero and length_cm is NA
# In order to not get any NA CPUEs in unit biomass because length is NA (I want them instead
# to be 0, as the numbers-CPUE is), I will replace length_cm == NA with length_cm == 0 before
# calculating biomass cpue
hlcodL <- hlcodL %>% mutate(length_cm2 = replace_na(length_cm, 0))
# Standardize length in the haul-data and calculate weight
hlcodL <- hlcodL %>%
mutate(weight_kg = (a*length_cm2^b)/1000) %>%
mutate(CPUEun_kg = weight_kg*CPUEun)
# Plot and check it's correct also in this data
ggplot(hlcodL, aes(weight_kg, length_cm2)) +
geom_point() +
facet_wrap(~Year)
# Hmm, some unrealistic weights actually
hlcodL %>% arrange(desc(weight_kg)) %>% as.data.frame() %>% head(100)
#> RecordType Survey Quarter Country Ship Gear SweepLngt GearEx DoorType
#> 1 HL BITS 1 DEN 26D4 GRT NA S NA
#> 2 HL BITS 1 DEN 26D4 GRT NA S NA
#> 3 HL BITS 1 DEN 26D4 GRT NA S NA
#> 4 HL BITS 1 DEN 26D4 GRT NA S NA
#> 5 HL BITS 1 DEN 26D4 GRT NA S NA
#> 6 HL BITS 1 DEN 26D4 GRT NA S NA
#> 7 HL BITS 1 DEN 26D4 GRT NA S NA
#> 8 HL BITS 1 DEN 26D4 GRT NA S NA
#> 9 HL BITS 1 DEN 26D4 GRT NA S NA
#> 10 HL BITS 1 DEN 26D4 GRT NA S NA
#> 11 HL BITS 1 DEN 26D4 GRT NA S NA
#> 12 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 13 HL BITS 1 SWE 77AR FOT 185 <NA> NA
#> 14 HL BITS 1 DEN 26D4 GRT NA S NA
#> 15 HL BITS 1 DEN 26D4 GRT 60 <NA> NA
#> 16 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 17 HL BITS 1 SWE 77AR FOT 225 <NA> NA
#> 18 HL BITS 1 GFR 06S1 H20 NA <NA> NA
#> 19 HL BITS 1 DEN 26D4 TVL 75 S NA
#> 20 HL BITS 1 LAT 90MX LBT NA <NA> NA
#> 21 HL BITS 1 DEN 26D4 GRT 60 <NA> NA
#> 22 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 23 HL BITS 4 SWE 77AR GOV 75 <NA> NA
#> 24 HL BITS 4 DEN 26D4 TVL 75 S NA
#> 25 HL BITS 1 DEN 26D4 GRT NA S NA
#> 26 HL BITS 1 GFR 06S1 CHP NA <NA> NA
#> 27 HL BITS 4 SWE 77AR TVL 75 <NA> NA
#> 28 HL BITS 1 SWE 77AR GOV 100 <NA> NA
#> 29 HL BITS 1 SWE 77AR GOV 100 <NA> NA
#> 30 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 31 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 32 HL BITS 4 SWE 77AR FOT 225 <NA> NA
#> 33 HL BITS 4 SWE 77AR FOT 203 <NA> NA
#> 34 HL BITS 4 SWE 77AR GOV 100 <NA> NA
#> 35 HL BITS 1 SWE 77AR FOT 225 <NA> NA
#> 36 HL BITS 1 DEN 26D4 GRT 110 <NA> NA
#> 37 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 38 HL BITS 1 RUS RUJB TVL NA <NA> NA
#> 39 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 40 HL BITS 1 GFR 06SL TVS NA <NA> NA
#> 41 HL BITS 1 GFR 06S1 CHP NA <NA> NA
#> 42 HL BITS 1 GFR 06S1 CHP NA <NA> NA
#> 43 HL BITS 1 DEN 26D4 GRT 60 <NA> NA
#> 44 HL BITS 1 SWE 77AR FOT 225 <NA> NA
#> 45 HL BITS 1 GFR 06S1 H20 NA <NA> NA
#> 46 HL BITS 1 DEN 26D4 TVL NA S TRUE
#> 47 HL BITS 1 SWE 77AR FOT 185 <NA> NA
#> 48 HL BITS 1 DEN 26D4 TVL 75 S NA
#> 49 HL BITS 1 LAT 90MX LBT NA <NA> NA
#> 50 HL BITS 1 DEN 26D4 GRT 60 <NA> NA
#> 51 HL BITS 1 LAT LAIZ LBT NA <NA> NA
#> 52 HL BITS 4 SWE 77AR FOT 180 <NA> NA
#> 53 HL BITS 4 POL 67BC TVL 75 S NA
#> 54 HL BITS 1 RUS RUJB HAK NA <NA> NA
#> 55 HL BITS 1 RUS RUJB HAK NA <NA> NA
#> 56 HL BITS 1 DEN 26D4 TVL NA S TRUE
#> 57 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 58 HL BITS 4 DEN 26D4 TVL 75 S NA
#> 59 HL BITS 1 RUS RUEK DT NA <NA> NA
#> 60 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 61 HL BITS 1 DEN 26D4 TVL NA S TRUE
#> 62 HL BITS 1 DEN 26D4 GRT NA S NA
#> 63 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 64 HL BITS 1 SWE 77AR FOT 203 <NA> NA
#> 65 HL BITS 1 POL 67BC TVL NA <NA> NA
#> 66 HL BITS 1 SWE 77AR GOV 50 <NA> NA
#> 67 HL BITS 1 SWE 77AR FOT 225 <NA> NA
#> 68 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 69 HL BITS 1 RUS RUNT TVL NA <NA> NA
#> 70 HL BITS 1 SWE 77AR TVL 95 S TRUE
#> 71 HL BITS 1 GFR 06S1 CHP NA <NA> NA
#> 72 HL BITS 4 GFR 06S1 H20 NA <NA> NA
#> 73 HL BITS 4 SWE 77AR FOT 185 <NA> NA
#> 74 HL BITS 4 SWE 77AR GOV 75 <NA> NA
#> 75 HL BITS 1 POL 67BC TVL NA <NA> NA
#> 76 HL BITS 1 RUS RUJB HAK NA <NA> NA
#> 77 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 78 HL BITS 1 GFR 06S1 H20 NA <NA> NA
#> 79 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 80 HL BITS 1 SWE 77AR FOT 203 <NA> NA
#> 81 HL BITS 1 POL 67BC TVL 75 S TRUE
#> 82 HL BITS 1 DEN 26D4 GRT NA S NA
#> 83 HL BITS 4 SWE 77AR FOT 225 <NA> NA
#> 84 HL BITS 1 LAT 90MX LBT NA <NA> NA
#> 85 HL BITS 1 RUS RUNT TVL NA <NA> NA
#> 86 HL BITS 1 DEN 26D4 GRT NA S NA
#> 87 HL BITS 1 RUS RUEK DT NA <NA> NA
#> 88 HL BITS 1 DEN 26D4 GRT 60 <NA> NA
#> 89 HL BITS 1 POL 67BC P20 NA <NA> NA
#> 90 HL BITS 1 DEN 26D4 GRT NA <NA> NA
#> 91 HL BITS 1 RUS RUNT HAK NA <NA> NA
#> 92 HL BITS 1 GFR 06S1 H20 NA <NA> NA
#> 93 HL BITS 1 GFR 06S1 TVS NA <NA> NA
#> 94 HL BITS 4 DEN 26D4 TVL NA S TRUE
#> 95 HL BITS 4 DEN 26D4 TVL NA S TRUE
#> 96 HL BITS 4 RUS RUJB TVL NA <NA> NA
#> 97 HL BITS 1 DEN 26D4 GRT 110 <NA> NA
#> 98 HL BITS 1 RUS RUJB TVL NA <NA> NA
#> 99 HL BITS 4 DEN 26D4 TVL NA S TRUE
#> 100 HL BITS 1 GFR 06S1 H20 NA <NA> NA
#> StNo HaulNo Year SpecCodeType SpecCode SpecVal Sex TotalNo CatIdentifier
#> 1 83 40 1998 T 164712 1 <NA> 17 1
#> 2 83 40 1998 T 164712 1 <NA> 17 1
#> 3 83 40 1998 T 164712 1 <NA> 17 1
#> 4 83 40 1998 T 164712 1 <NA> 17 1
#> 5 83 40 1998 T 164712 1 <NA> 17 1
#> 6 83 40 1998 T 164712 1 <NA> 17 1
#> 7 83 40 1998 T 164712 1 <NA> 17 1
#> 8 83 40 1998 T 164712 1 <NA> 17 1
#> 9 83 40 1998 T 164712 1 <NA> 17 1
#> 10 83 40 1998 T 164712 1 <NA> 17 1
#> 11 83 40 1998 T 164712 1 <NA> 17 1
#> 12 45 25 1996 W 126436 1 <NA> 93 1
#> 13 80 32 1993 W 126436 1 <NA> 454 1
#> 14 83 40 1998 T 164712 1 <NA> 17 1
#> 15 43 22 1994 T 164712 1 <NA> 37 1
#> 16 9 5 1996 W 126436 1 <NA> 406 1
#> 17 191 3 1998 T 164712 1 <NA> 546 1
#> 18 46 20 1993 W 126436 1 <NA> 326 1
#> 19 71 36 2003 T 164712 1 <NA> 255 1
#> 20 000001 19 1991 W 126436 1 <NA> 282 1
#> 21 117 50 1994 T 164712 1 <NA> 775 1
#> 22 75 40 1996 W 126436 1 <NA> 29 1
#> 23 584 18 2000 W 126436 1 <NA> 454 1
#> 24 10 4 2002 T 164712 1 <NA> 1958 1
#> 25 136 64 1998 T 164712 1 <NA> 130 1
#> 26 26073 50 1991 W 126436 1 <NA> 77 1
#> 27 573 11 2002 W 126436 1 <NA> 3090 1
#> 28 220 21 2000 W 126436 1 <NA> 1076 1
#> 29 237 34 2000 W 126436 1 <NA> 638 1
#> 30 106 57 1991 W 126436 1 <NA> 51 1
#> 31 111 47 1996 W 126436 1 <NA> 78 1
#> 32 231 26 1996 W 126436 1 <NA> 54 1
#> 33 247 2 1995 W 126436 1 <NA> 2736 1
#> 34 652 17 1999 T 164712 1 <NA> 1484 1
#> 35 207 15 1998 T 164712 1 <NA> 20 1
#> 36 162 72 1993 W 126436 1 <NA> 128 1
#> 37 53 30 1996 W 126436 1 <NA> 197 1
#> 38 <NA> 13 2006 W 126436 1 <NA> 24 1
#> 39 57 23 1995 W 126436 1 <NA> 223 1
#> 40 24316 41 2008 T 164712 1 <NA> 864 1
#> 41 26021 29 1992 W 126436 1 <NA> 68 1
#> 42 26073 50 1991 W 126436 1 <NA> 77 1
#> 43 74 39 1994 T 164712 1 <NA> 457 1
#> 44 222 26 1998 T 164712 1 <NA> 126 1
#> 45 44 24 1994 T 164712 1 <NA> 260 1
#> 46 95 66 2005 W 126436 1 <NA> 764 1
#> 47 58 10 1993 W 126436 1 <NA> 85 1
#> 48 6 3 2001 T 164712 1 <NA> 132 1
#> 49 000001 12 1991 W 126436 1 <NA> 49 1
#> 50 17 9 1993 W 126436 1 <NA> 281 1
#> 51 000001 6 1993 W 126436 1 <NA> 22 1
#> 52 252 21 1993 W 126436 1 <NA> 5932 1
#> 53 26132 24 2008 T 164712 1 <NA> 1696 1
#> 54 <NA> 35 1998 T 164712 1 <NA> 1214 1
#> 55 <NA> 33 1998 T 164712 1 <NA> 302 1
#> 56 19 17 2009 W 126436 1 <NA> 972 1
#> 57 81 44 1991 W 126436 1 <NA> 164 1
#> 58 94 46 2000 T 164712 1 <NA> 180 1
#> 59 <NA> 13 1996 W 126436 1 <NA> 194 1
#> 60 33 14 1995 W 126436 1 <NA> 32 1
#> 61 100 1 2010 W 126436 1 <NA> 552 1
#> 62 16 8 2000 W 126436 1 <NA> 85 1
#> 63 66 36 1996 W 126436 1 <NA> 43 1
#> 64 74 23 1996 W 126436 1 <NA> 936 1
#> 65 1 1 2004 W 126436 1 <NA> 470 1
#> 66 85 37 1993 W 126436 1 <NA> 1332 1
#> 67 199 8 1998 T 164712 1 <NA> 1016 1
#> 68 123 46 1995 W 126436 1 <NA> 231 1
#> 69 60 17 2010 T 164712 1 <NA> 566 1
#> 70 267 52 2009 W 126436 1 U 650 1
#> 71 26021 29 1992 W 126436 1 <NA> 68 1
#> 72 25 72 1994 T 164712 1 <NA> 428 1
#> 73 287 32 1994 W 126436 1 <NA> 2924 1
#> 74 594 27 2000 W 126436 1 <NA> 842 1
#> 75 2 2 2004 W 126436 1 <NA> 286 1
#> 76 <NA> 37 1998 T 164712 1 <NA> 668 1
#> 77 11 3 1995 W 126436 1 <NA> 80 1
#> 78 73 34 1994 T 164712 1 <NA> 764 1
#> 79 51 29 1996 W 126436 1 <NA> 212 1
#> 80 95 44 1996 W 126436 1 <NA> 248 1
#> 81 26211 3 2011 W 126436 1 U 1130 1
#> 82 18 7 1997 T 164712 1 <NA> 75 1
#> 83 756 26 1997 W 126436 1 <NA> 22 1
#> 84 000001 11 1991 W 126436 1 <NA> 82 1
#> 85 60 23 2010 T 164712 1 <NA> 6475 1
#> 86 130 61 1998 T 164712 1 <NA> 493 1
#> 87 <NA> 19 1995 W 126436 1 <NA> 466 1
#> 88 63 32 1994 T 164712 1 <NA> 40 1
#> 89 Mar 18 1994 T 164712 1 <NA> 48 1
#> 90 46 26 1996 W 126436 1 <NA> 110 1
#> 91 <NA> 5 1999 T 164712 1 <NA> 94 1
#> 92 48 22 1993 W 126436 1 <NA> 586 1
#> 93 902 34 2001 T 164712 1 <NA> 278 1
#> 94 179 16 2009 W 126436 1 <NA> 943 1
#> 95 7 32 2006 W 126436 1 <NA> 234 2
#> 96 <NA> 60 2003 T 164712 1 <NA> 194 1
#> 97 79 55 1992 W 126436 1 <NA> 4 1
#> 98 56 40 2011 T 164712 1 <NA> 4360 1
#> 99 39 42 2011 W 126436 1 <NA> 710 1
#> 100 47 51 1996 W 126436 1 <NA> 640 1
#> NoMeas SubFactor SubWgt CatCatchWgt LngtCode LngtClass HLNoAtLngt DevStage
#> 1 17 1 NA 13275 0 335 1 <NA>
#> 2 17 1 NA 13275 0 285 1 <NA>
#> 3 17 1 NA 13275 0 225 2 <NA>
#> 4 17 1 NA 13275 0 220 1 <NA>
#> 5 17 1 NA 13275 0 215 1 <NA>
#> 6 17 1 NA 13275 0 180 1 <NA>
#> 7 17 1 NA 13275 0 175 1 <NA>
#> 8 17 1 NA 13275 0 160 1 <NA>
#> 9 17 1 NA 13275 0 150 2 <NA>
#> 10 17 1 NA 13275 0 145 3 <NA>
#> 11 17 1 NA 13275 0 140 1 <NA>
#> 12 93 1 113900 113900 . 1360 1 <NA>
#> 13 454 1 NA 234500 1 127 1 <NA>
#> 14 17 1 NA 13275 0 130 2 <NA>
#> 15 37 1 NA 1033 1 127 1 <NA>
#> 16 406 1 257280 257280 . 1230 1 <NA>
#> 17 273 1 NA 484399 1 121 2 <NA>
#> 18 163 1 NA 2190 1 116 2 <NA>
#> 19 255 1 NA 158200 1 121 1 <NA>
#> 20 141 1 NA 2390 1 112 2 <NA>
#> 21 775 1 NA 5718 1 118 1 <NA>
#> 22 29 1 48300 48300 . 1170 1 <NA>
#> 23 227 1 NA 268580 1 118 2 <NA>
#> 24 1958 1 NA 1040200 1 118 1 <NA>
#> 25 130 1 NA 157570 1 116 1 <NA>
#> 26 77 1 NA 1540 1 110 1 <NA>
#> 27 1545 1 NA 1302400 1 117 2 <NA>
#> 28 538 1 NA 303000 1 116 2 <NA>
#> 29 319 1 NA 158200 1 115 2 <NA>
#> 30 51 1 NA 599 1 108 1 <NA>
#> 31 78 1 119000 119000 . 1130 1 <NA>
#> 32 27 1 NA 96200 1 113 2 <NA>
#> 33 1368 1 NA 607800 1 111 2 <NA>
#> 34 742 1 NA 267459 1 113 2 <NA>
#> 35 10 1 NA 76000 1 113 2 <NA>
#> 36 128 1 NA 1079 1 109 1 <NA>
#> 37 197 1 192900 192900 . 1120 1 <NA>
#> 38 12 1 NA 39544 1 118 2 <NA>
#> 39 223 1 149100 149100 . 1100 1 <NA>
#> 40 864 1 NA 247830 1 119 1 <NA>
#> 41 68 1 NA 2535 1 109 1 <NA>
#> 42 77 1 NA 1540 1 106 1 <NA>
#> 43 457 1 NA 42 1 112 1 <NA>
#> 44 63 1 NA 48400 1 111 2 <NA>
#> 45 130 1 NA 2374 1 111 2 <NA>
#> 46 764 1 139400 139400 . 1150 1 <NA>
#> 47 85 1 NA 46000 1 107 1 <NA>
#> 48 132 1 NA 61000 1 111 1 <NA>
#> 49 49 1 NA 1013 1 104 1 <NA>
#> 50 277 1 NA 1297 1 106 1 <NA>
#> 51 22 1 NA 219 1 106 1 <NA>
#> 52 1499 1 NA 5688600 1 106 3 <NA>
#> 53 848 1 NA 968860 1 116 2 <NA>
#> 54 390 1 NA 16278 1 109 3 <NA>
#> 55 151 1 NA 2962 1 109 2 <NA>
#> 56 972 1 322476 322476 . 1130 1 <NA>
#> 57 164 1 NA 3531 1 103 1 <NA>
#> 58 180 1 NA 46605 1 109 1 <NA>
#> 59 97 1 NA 3360 1 108 2 <NA>
#> 60 32 1 47100 47100 . 1060 1 <NA>
#> 61 552 1 419300 419300 . 1130 1 <NA>
#> 62 85 1 NA 37470 1 108 1 <NA>
#> 63 43 1 67365 67365 . 1070 1 <NA>
#> 64 468 1 NA 597000 1 107 2 <NA>
#> 65 235 1 NA 98500 1 110 2 <NA>
#> 66 1332 1 NA 574000 1 104 1 <NA>
#> 67 508 1 NA 268000 1 107 2 <NA>
#> 68 231 1 204000 204000 . 1050 1 <NA>
#> 69 283 1 NA 369740 1 112 2 <NA>
#> 70 325 1 NA 253940 1 111 2 <NA>
#> 71 68 1 NA 2535 1 104 1 <NA>
#> 72 214 1 NA 326000 1 107 2 <NA>
#> 73 1462 1 NA 1872800 1 107 2 <NA>
#> 74 421 1 NA 382800 1 107 2 <NA>
#> 75 143 1 NA 185600 1 109 2 <NA>
#> 76 291 1 NA 9063 1 106 2 <NA>
#> 77 80 1 66500 66500 . 1040 1 <NA>
#> 78 382 1 NA 10120 1 106 2 <NA>
#> 79 212 1 324500 324500 . 1050 1 <NA>
#> 80 124 1 NA 419000 1 105 2 <NA>
#> 81 565 1 216000 432000 1 110 2 <NA>
#> 82 75 1 NA 108824 1 105 1 <NA>
#> 83 11 1 NA 74600 1 105 2 <NA>
#> 84 41 1 NA 1500 1 100 2 <NA>
#> 85 413 1 NA 5844000 1 110 16 <NA>
#> 86 493 1 NA 577785 1 105 1 <NA>
#> 87 233 1 NA 4381 1 103 2 <NA>
#> 88 40 1 NA 761 1 105 1 <NA>
#> 89 24 1 NA 28400 1 105 2 <NA>
#> 90 110 1 108900 108900 . 1040 1 <NA>
#> 91 47 1 NA 1155 1 104 2 <NA>
#> 92 293 1 NA 2710 1 101 2 <NA>
#> 93 139 1 NA 1266 1 105 2 <NA>
#> 94 943 1 430200 430200 . 1080 1 <NA>
#> 95 234 1 121600 121600 . 1090 1 <NA>
#> 96 97 1 NA 137200 1 105 2 <NA>
#> 97 4 1 NA 151 1 101 1 <NA>
#> 98 639 1 NA 1289466 1 108 2 <NA>
#> 99 710 1 323900 323900 . 1080 1 <NA>
#> 100 165 1 NA 5280 1 103 4 <NA>
#> LenMeasType DateofCalculation Valid_Aphia Ship2 Ship3
#> 1 NA 20140617 126436 DAN2 DAN2
#> 2 NA 20140617 126436 DAN2 DAN2
#> 3 NA 20140617 126436 DAN2 DAN2
#> 4 NA 20140617 126436 DAN2 DAN2
#> 5 NA 20140617 126436 DAN2 DAN2
#> 6 NA 20140617 126436 DAN2 DAN2
#> 7 NA 20140617 126436 DAN2 DAN2
#> 8 NA 20140617 126436 DAN2 DAN2
#> 9 NA 20140617 126436 DAN2 DAN2
#> 10 NA 20140617 126436 DAN2 DAN2
#> 11 NA 20140617 126436 DAN2 DAN2
#> 12 NA 20190208 126436 DAN2 DAN2
#> 13 NA 20211203 126436 ARG ARG
#> 14 NA 20140617 126436 DAN2 DAN2
#> 15 NA 20161213 126436 DAN2 DAN2
#> 16 NA 20190208 126436 DAN2 DAN2
#> 17 NA 20140617 126436 ARG ARG
#> 18 NA 20211203 126436 SOL SOL
#> 19 NA 20131108 126436 DAN2 DAN2
#> 20 NA 20211203 126436 90MX 90MX
#> 21 NA 20161213 126436 DAN2 DAN2
#> 22 NA 20190208 126436 DAN2 DAN2
#> 23 NA 20131112 126436 ARG ARG
#> 24 NA 20131113 126436 DAN2 DAN2
#> 25 NA 20140617 126436 DAN2 DAN2
#> 26 NA 20211203 126436 SOL SOL
#> 27 NA 20131113 126436 ARG ARG
#> 28 NA 20190228 126436 ARG ARG
#> 29 NA 20190228 126436 ARG ARG
#> 30 NA 20211203 126436 DAN2 DAN2
#> 31 NA 20190208 126436 DAN2 DAN2
#> 32 NA 20161115 126436 ARG ARG
#> 33 NA 20161115 126436 ARG ARG
#> 34 NA 20131112 126436 ARG ARG
#> 35 NA 20140617 126436 ARG ARG
#> 36 NA 20211203 126436 DAN2 DAN2
#> 37 NA 20190208 126436 DAN2 DAN2
#> 38 NA 20200115 126436 ATLD ATLD
#> 39 NA 20190207 126436 DAN2 DAN2
#> 40 NA 20180423 126436 SOL2 SOL2
#> 41 NA 20211203 126436 SOL SOL
#> 42 NA 20211203 126436 SOL SOL
#> 43 NA 20161213 126436 DAN2 DAN2
#> 44 NA 20140617 126436 ARG ARG
#> 45 NA 20161213 126436 SOL SOL
#> 46 NA 20131204 126436 DAN2 DAN2
#> 47 NA 20211203 126436 ARG ARG
#> 48 NA 20160714 126436 DAN2 DAN2
#> 49 NA 20211203 126436 90MX 90MX
#> 50 NA 20211203 126436 DAN2 DAN2
#> 51 NA 20211203 126436 LAIZ LAIZ
#> 52 NA 20131111 126436 ARG ARG
#> 53 NA 20180507 126436 BAL BAL
#> 54 NA 20140617 126436 ATLD ATLD
#> 55 NA 20140617 126436 ATLD ATLD
#> 56 NA 20131216 126436 DAN2 DAN2
#> 57 NA 20211203 126436 DAN2 DAN2
#> 58 NA 20131112 126436 DAN2 DAN2
#> 59 NA 20190208 126436 RUEK RUEK
#> 60 NA 20190207 126436 DAN2 DAN2
#> 61 NA 20180423 126436 DAN2 DAN2
#> 62 NA 20190228 126436 DAN2 DAN2
#> 63 NA 20190208 126436 DAN2 DAN2
#> 64 NA 20190208 126436 ARG ARG
#> 65 NA 20160721 126436 BAL BAL
#> 66 NA 20211203 126436 ARG ARG
#> 67 NA 20140617 126436 ARG ARG
#> 68 NA 20190207 126436 DAN2 DAN2
#> 69 NA 20180423 126436 ATL ATL
#> 70 NA 20131216 126436 ARG ARG
#> 71 NA 20211203 126436 SOL SOL
#> 72 NA 20161115 126436 SOL SOL
#> 73 NA 20161115 126436 ARG ARG
#> 74 NA 20131112 126436 ARG ARG
#> 75 NA 20160721 126436 BAL BAL
#> 76 NA 20140617 126436 ATLD ATLD
#> 77 NA 20190207 126436 DAN2 DAN2
#> 78 NA 20161213 126436 SOL SOL
#> 79 NA 20190208 126436 DAN2 DAN2
#> 80 NA 20190208 126436 ARG ARG
#> 81 NA 20140611 126436 BAL BAL
#> 82 NA 20161115 126436 DAN2 DAN2
#> 83 NA 20161115 126436 ARG ARG
#> 84 NA 20211203 126436 90MX 90MX
#> 85 NA 20180423 126436 ATL ATL
#> 86 NA 20140617 126436 DAN2 DAN2
#> 87 NA 20190207 126436 RUEK RUEK
#> 88 NA 20161213 126436 DAN2 DAN2
#> 89 NA 20161213 126436 BAL BAL
#> 90 NA 20190208 126436 DAN2 DAN2
#> 91 NA 20140616 126436 ATL ATL
#> 92 NA 20211203 126436 SOL SOL
#> 93 NA 20160714 126436 SOL SOL
#> 94 NA 20160721 126436 DAN2 DAN2
#> 95 NA 20131216 126436 DAN2 DAN2
#> 96 NA 20131113 126436 ATLD ATLD
#> 97 NA 20211203 126436 DAN2 DAN2
#> 98 NA 20140611 126436 ATLD ATLD
#> 99 NA 20140617 126436 DAN2 DAN2
#> 100 NA 20190208 126436 SOL SOL
#> IDx sub_div Rect HaulVal StdSpecRecCode
#> 1 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 2 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 3 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 4 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 5 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 6 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 7 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 8 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 9 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 10 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 11 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 12 1996.1.DEN.26D4.GRT.45.25 26 41H0 V 1
#> 13 1993.1.SWE.77AR.FOT.80.32 25 39G5 V 1
#> 14 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 15 1994.1.DEN.26D4.GRT.43.22 26 41H0 V 1
#> 16 1996.1.DEN.26D4.GRT.9.5 25 38G5 V 1
#> 17 1998.1.SWE.77AR.FOT.191.3 25 40G5 V 1
#> 18 1993.1.GFR.06S1.H20.46.20 24 39G4 V 1
#> 19 2003.1.DEN.26D4.TVL.71.36 25 39G5 V 1
#> 20 1991.1.LAT.90MX.LBT.000001.19 26 40H0 V 1
#> 21 1994.1.DEN.26D4.GRT.117.50 25 38G5 V 1
#> 22 1996.1.DEN.26D4.GRT.75.40 25 39G6 V 1
#> 23 2000.4.SWE.77AR.GOV.584.18 25 40G5 V 1
#> 24 2002.4.DEN.26D4.TVL.10.4 24 39G4 V 1
#> 25 1998.1.DEN.26D4.GRT.136.64 26 39G8 V 1
#> 26 1991.1.GFR.06S1.CHP.26073.50 26 39G8 V 1
#> 27 2002.4.SWE.77AR.TVL.573.11 25 41G7 V 1
#> 28 2000.1.SWE.77AR.GOV.220.21 24 39G3 V 1
#> 29 2000.1.SWE.77AR.GOV.237.34 25 40G5 V 1
#> 30 1991.1.DEN.26D4.GRT.106.57 25 39G7 V 1
#> 31 1996.1.DEN.26D4.GRT.111.47 25 39G5 V 1
#> 32 1996.4.SWE.77AR.FOT.231.26 28 43G8 V 1
#> 33 1995.4.SWE.77AR.FOT.247.2 25 40G5 V 1
#> 34 1999.4.SWE.77AR.GOV.652.17 25 40G5 V 1
#> 35 1998.1.SWE.77AR.FOT.207.15 25 40G7 V 1
#> 36 1993.1.DEN.26D4.GRT.162.72 25 38G5 V 1
#> 37 1996.1.DEN.26D4.GRT.53.30 26 41G9 V 1
#> 38 2006.1.RUS.RUJB.TVL.NA.13 26 39H0 V 1
#> 39 1995.1.DEN.26D4.GRT.57.23 26 41G9 V 1
#> 40 2008.1.GFR.06SL.TVS.24316.41 24 39G4 V 1
#> 41 1992.1.GFR.06S1.CHP.26021.29 26 41G8 V 1
#> 42 1991.1.GFR.06S1.CHP.26073.50 26 39G8 V 1
#> 43 1994.1.DEN.26D4.GRT.74.39 26 41G8 V 1
#> 44 1998.1.SWE.77AR.FOT.222.26 28 43H0 V 1
#> 45 1994.1.GFR.06S1.H20.44.24 24 39G3 V 1
#> 46 2005.1.DEN.26D4.TVL.95.66 25 39G5 V 1
#> 47 1993.1.SWE.77AR.FOT.58.10 25 41G7 V 1
#> 48 2001.1.DEN.26D4.TVL.6.3 24 39G3 V 1
#> 49 1991.1.LAT.90MX.LBT.000001.12 26 39H0 V 1
#> 50 1993.1.DEN.26D4.GRT.17.9 24 39G4 V 1
#> 51 1993.1.LAT.LAIZ.LBT.000001.6 26 40H0 V 1
#> 52 1993.4.SWE.77AR.FOT.252.21 25 41G7 V 1
#> 53 2008.4.POL.67BC.TVL.26132.24 26 38G8 V 3
#> 54 1998.1.RUS.RUJB.HAK.NA.35 26 40G9 V 1
#> 55 1998.1.RUS.RUJB.HAK.NA.33 26 40G9 V 1
#> 56 2009.1.DEN.26D4.TVL.19.17 24 39G4 V 1
#> 57 1991.1.DEN.26D4.GRT.81.44 26 41G9 V 1
#> 58 2000.4.DEN.26D4.TVL.94.46 25 39G6 V 1
#> 59 1996.1.RUS.RUEK.DT.NA.13 26 39G8 V 1
#> 60 1995.1.DEN.26D4.GRT.33.14 26 41G8 V 1
#> 61 2010.1.DEN.26D4.TVL.100.1 25 39G6 V 1
#> 62 2000.1.DEN.26D4.GRT.16.8 25 40G5 V 1
#> 63 1996.1.DEN.26D4.GRT.66.36 25 39G7 V 1
#> 64 1996.1.SWE.77AR.FOT.74.23 25 40G6 V 1
#> 65 2004.1.POL.67BC.TVL.1.1 26 38G8 V 3
#> 66 1993.1.SWE.77AR.GOV.85.37 24 38G3 V 1
#> 67 1998.1.SWE.77AR.FOT.199.8 24 39G3 V 1
#> 68 1995.1.DEN.26D4.GRT.123.46 25 40G6 V 1
#> 69 2010.1.RUS.RUNT.TVL.60.17 26 39G8 V 1
#> 70 2009.1.SWE.77AR.TVL.267.52 25 40G4 V 1
#> 71 1992.1.GFR.06S1.CHP.26021.29 26 41G8 V 1
#> 72 1994.4.GFR.06S1.H20.25.72 24 38G3 V 1
#> 73 1994.4.SWE.77AR.FOT.287.32 25 40G6 V 1
#> 74 2000.4.SWE.77AR.GOV.594.27 25 40G7 V 1
#> 75 2004.1.POL.67BC.TVL.2.2 26 39G8 V 3
#> 76 1998.1.RUS.RUJB.HAK.NA.37 26 40H0 V 1
#> 77 1995.1.DEN.26D4.GRT.11.3 25 39G7 V 1
#> 78 1994.1.GFR.06S1.H20.73.34 25 40G4 V 1
#> 79 1996.1.DEN.26D4.GRT.51.29 26 41G9 V 1
#> 80 1996.1.SWE.77AR.FOT.95.44 27 43G7 V 1
#> 81 2011.1.POL.67BC.TVL.26211.3 26 38G9 V 1
#> 82 1997.1.DEN.26D4.GRT.18.7 25 40G6 V 1
#> 83 1997.4.SWE.77AR.FOT.756.26 26 41G8 V 1
#> 84 1991.1.LAT.90MX.LBT.000001.11 26 41H0 V 1
#> 85 2010.1.RUS.RUNT.TVL.60.23 26 38G9 V 1
#> 86 1998.1.DEN.26D4.GRT.130.61 26 40G9 V 1
#> 87 1995.1.RUS.RUEK.DT.NA.19 26 38G9 V 1
#> 88 1994.1.DEN.26D4.GRT.63.32 26 39G8 V 1
#> 89 1994.1.POL.67BC.P20.Mar.18 26 38G8 V 1
#> 90 1996.1.DEN.26D4.GRT.46.26 26 41G9 V 1
#> 91 1999.1.RUS.RUNT.HAK.NA.5 26 38G9 V 1
#> 92 1993.1.GFR.06S1.H20.48.22 24 39G4 V 1
#> 93 2001.1.GFR.06S1.TVS.902.34 24 39G4 V 3
#> 94 2009.4.DEN.26D4.TVL.179.16 25 38G6 V 1
#> 95 2006.4.DEN.26D4.TVL.7.32 25 38G5 V 1
#> 96 2003.4.RUS.RUJB.TVL.NA.60 26 39H0 V 1
#> 97 1992.1.DEN.26D4.GRT.79.55 26 39G8 V 1
#> 98 2011.1.RUS.RUJB.TVL.56.40 26 39G9 V 1
#> 99 2011.4.DEN.26D4.TVL.39.42 25 40G4 V 1
#> 100 1996.1.GFR.06S1.H20.47.51 24 39G4 V 1
#> BySpecRecCode Fishing.line DataType HaulDur GroundSpeed
#> 1 1 -9.00 R 60 3.0
#> 2 1 -9.00 R 60 3.0
#> 3 1 -9.00 R 60 3.0
#> 4 1 -9.00 R 60 3.0
#> 5 1 -9.00 R 60 3.0
#> 6 1 -9.00 R 60 3.0
#> 7 1 -9.00 R 60 3.0
#> 8 1 -9.00 R 60 3.0
#> 9 1 -9.00 R 60 3.0
#> 10 1 -9.00 R 60 3.0
#> 11 1 -9.00 R 60 3.0
#> 12 1 -9.00 R 60 3.4
#> 13 1 83.00 C 60 3.0
#> 14 1 -9.00 R 60 3.0
#> 15 1 -9.00 C 60 -9.0
#> 16 1 -9.00 R 60 3.6
#> 17 1 83.00 C 30 3.3
#> 18 1 36.00 C 30 4.0
#> 19 1 63.46 R 30 3.0
#> 20 1 28.00 C 30 -9.0
#> 21 1 -9.00 C 60 -9.0
#> 22 1 -9.00 R 60 3.2
#> 23 1 160.00 C 30 3.7
#> 24 1 63.46 R 30 3.1
#> 25 1 -9.00 R 60 3.3
#> 26 1 35.20 C 60 3.5
#> 27 1 63.46 C 30 3.0
#> 28 1 160.00 C 30 3.5
#> 29 1 160.00 C 30 3.5
#> 30 1 -9.00 C 60 -9.0
#> 31 1 -9.00 R 60 3.5
#> 32 1 83.00 C 30 3.3
#> 33 1 83.00 C 30 3.7
#> 34 1 160.00 C 30 3.3
#> 35 1 83.00 C 30 3.3
#> 36 1 -9.00 C 60 -9.0
#> 37 1 -9.00 R 60 3.1
#> 38 1 63.46 C 30 3.0
#> 39 1 -9.00 R 60 -9.0
#> 40 1 33.22 R 30 3.2
#> 41 1 35.20 C 60 3.4
#> 42 1 35.20 C 60 3.5
#> 43 1 -9.00 C 60 -9.0
#> 44 1 83.00 C 30 3.4
#> 45 1 36.00 C 30 3.8
#> 46 1 63.46 R 30 3.0
#> 47 1 83.00 C 60 3.5
#> 48 1 63.46 R 30 3.0
#> 49 1 28.00 C 60 -9.0
#> 50 1 -9.00 C 59 -9.0
#> 51 1 28.00 C 60 -9.0
#> 52 1 83.00 C 30 3.0
#> 53 0 63.46 C 30 3.0
#> 54 1 -9.00 C 30 3.6
#> 55 1 -9.00 C 30 3.6
#> 56 1 63.46 R 30 2.9
#> 57 1 -9.00 C 60 -9.0
#> 58 1 63.46 R 31 2.9
#> 59 1 -9.00 C 30 3.0
#> 60 1 -9.00 R 60 -9.0
#> 61 1 63.46 R 30 3.0
#> 62 1 -9.00 R 60 2.9
#> 63 1 -9.00 R 60 3.1
#> 64 1 83.00 C 30 3.4
#> 65 0 63.46 C 30 -9.0
#> 66 1 160.00 C 60 2.9
#> 67 1 83.00 C 30 3.5
#> 68 1 -9.00 R 60 -9.0
#> 69 1 63.46 C 30 2.9
#> 70 1 63.46 C 30 3.0
#> 71 1 35.20 C 60 3.4
#> 72 1 36.00 C 30 3.4
#> 73 1 83.00 C 30 3.2
#> 74 1 160.00 C 30 3.5
#> 75 0 63.46 C 30 -9.0
#> 76 1 -9.00 C 60 3.9
#> 77 1 -9.00 R 60 -9.0
#> 78 1 36.00 C 30 3.2
#> 79 1 -9.00 R 60 3.0
#> 80 1 83.00 C 30 3.8
#> 81 1 63.46 C 30 3.0
#> 82 1 -9.00 R 60 3.3
#> 83 1 83.00 C 30 3.3
#> 84 1 28.00 C 30 -9.0
#> 85 1 63.46 C 15 3.0
#> 86 1 -9.00 R 60 3.2
#> 87 1 -9.00 C 30 3.3
#> 88 1 -9.00 C 60 -9.0
#> 89 0 39.80 C 30 -9.0
#> 90 1 -9.00 R 60 3.3
#> 91 1 -9.00 C 30 3.5
#> 92 1 36.00 C 30 3.6
#> 93 1 33.22 C 30 3.2
#> 94 1 63.46 R 30 2.8
#> 95 1 63.46 R 31 3.3
#> 96 1 63.46 C 30 3.0
#> 97 1 -9.00 C 60 -9.0
#> 98 1 63.46 C 30 3.0
#> 99 1 63.46 R 29 1.7
#> 100 1 36.00 C 30 4.0
#> haul.id ShootLat ShootLong Depth Species
#> 1 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 2 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 3 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 4 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 5 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 6 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 7 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 8 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 9 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 10 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 11 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 12 1996:1:DEN:DAN2:GRT:45:25 56.3700 20.0666 70 Gadus morhua
#> 13 1993:1:SWE:ARG:FOT:80:32 55.4120 15.3205 92 Gadus morhua
#> 14 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 71 Gadus morhua
#> 15 1994:1:DEN:DAN2:GRT:43:22 56.4500 20.2000 81 Gadus morhua
#> 16 1996:1:DEN:DAN2:GRT:9:5 54.5750 15.3200 60 Gadus morhua
#> 17 1998:1:SWE:ARG:FOT:191:3 55.8218 15.4220 53 Gadus morhua
#> 18 1993:1:GFR:SOL:H20:46:20 55.1333 14.1667 48 Gadus morhua
#> 19 2003:1:DEN:DAN2:TVL:71:36 55.3709 15.5140 97 Gadus morhua
#> 20 1991:1:LAT:90MX:LBT:000001:19 55.6167 20.0833 80 Gadus morhua
#> 21 1994:1:DEN:DAN2:GRT:117:50 54.7833 15.9500 57 Gadus morhua
#> 22 1996:1:DEN:DAN2:GRT:75:40 55.1983 16.4916 56 Gadus morhua
#> 23 2000:4:SWE:ARG:GOV:584:18 55.8150 15.3917 52 Gadus morhua
#> 24 2002:4:DEN:DAN2:TVL:10:4 55.4908 14.6121 58 Gadus morhua
#> 25 1998:1:DEN:DAN2:GRT:136:64 55.4800 18.4683 90 Gadus morhua
#> 26 1991:1:GFR:SOL:CHP:26073:50 55.2500 18.3500 81 Gadus morhua
#> 27 2002:4:SWE:ARG:TVL:573:11 56.1460 17.7615 63 Gadus morhua
#> 28 2000:1:SWE:ARG:GOV:220:21 55.0100 13.9300 48 Gadus morhua
#> 29 2000:1:SWE:ARG:GOV:237:34 55.8467 15.5717 41 Gadus morhua
#> 30 1991:1:DEN:DAN2:GRT:106:57 55.3167 17.3167 80 Gadus morhua
#> 31 1996:1:DEN:DAN2:GRT:111:47 55.2150 15.6083 98 Gadus morhua
#> 32 1996:4:SWE:ARG:FOT:231:26 57.0833 18.9000 90 Gadus morhua
#> 33 1995:4:SWE:ARG:FOT:247:2 55.8323 15.5427 38 Gadus morhua
#> 34 1999:4:SWE:ARG:GOV:652:17 55.8400 15.5600 44 Gadus morhua
#> 35 1998:1:SWE:ARG:FOT:207:15 55.9271 17.1108 47 Gadus morhua
#> 36 1993:1:DEN:DAN2:GRT:162:72 54.8500 15.6667 79 Gadus morhua
#> 37 1996:1:DEN:DAN2:GRT:53:30 56.2566 19.5666 82 Gadus morhua
#> 38 2006:1:RUS:ATLD:TVL:NA:13 55.1583 20.0267 60 Gadus morhua
#> 39 1995:1:DEN:DAN2:GRT:57:23 56.3666 19.8666 89 Gadus morhua
#> 40 2008:1:GFR:SOL2:TVS:24316:41 55.1505 14.0718 46 Gadus morhua
#> 41 1992:1:GFR:SOL:CHP:26021:29 56.1500 18.5333 110 Gadus morhua
#> 42 1991:1:GFR:SOL:CHP:26073:50 55.2500 18.3500 81 Gadus morhua
#> 43 1994:1:DEN:DAN2:GRT:74:39 56.1167 18.2667 75 Gadus morhua
#> 44 1998:1:SWE:ARG:FOT:222:26 57.2520 20.7436 72 Gadus morhua
#> 45 1994:1:GFR:SOL:H20:44:24 55.0333 13.4333 48 Gadus morhua
#> 46 2005:1:DEN:DAN2:TVL:95:66 55.0966 15.2680 67 Gadus morhua
#> 47 1993:1:SWE:ARG:FOT:58:10 56.0352 17.7188 64 Gadus morhua
#> 48 2001:1:DEN:DAN2:TVL:6:3 55.0214 13.7642 47 Gadus morhua
#> 49 1991:1:LAT:90MX:LBT:000001:12 55.1167 20.3333 40 Gadus morhua
#> 50 1993:1:DEN:DAN2:GRT:17:9 55.0333 14.2167 48 Gadus morhua
#> 51 1993:1:LAT:LAIZ:LBT:000001:6 55.5667 20.6500 60 Gadus morhua
#> 52 1993:4:SWE:ARG:FOT:252:21 56.1133 17.5917 42 Gadus morhua
#> 53 2008:4:POL:BAL:TVL:26132:24 54.5400 18.8867 65 Gadus morhua
#> 54 1998:1:RUS:ATLD:HAK:NA:35 55.5167 19.8833 85 Gadus morhua
#> 55 1998:1:RUS:ATLD:HAK:NA:33 55.6167 19.6500 80 Gadus morhua
#> 56 2009:1:DEN:DAN2:TVL:19:17 55.3195 14.9859 72 Gadus morhua
#> 57 1991:1:DEN:DAN2:GRT:81:44 56.2333 19.5167 108 Gadus morhua
#> 58 2000:4:DEN:DAN2:TVL:94:46 55.1058 16.3850 63 Gadus morhua
#> 59 1996:1:RUS:RUEK:DT:NA:13 55.0833 18.4333 76 Gadus morhua
#> 60 1995:1:DEN:DAN2:GRT:33:14 56.4166 18.7166 94 Gadus morhua
#> 61 2010:1:DEN:DAN2:TVL:100:1 55.0897 16.3814 62 Gadus morhua
#> 62 2000:1:DEN:DAN2:GRT:16:8 55.7672 15.7198 54 Gadus morhua
#> 63 1996:1:DEN:DAN2:GRT:66:36 55.4566 17.8650 70 Gadus morhua
#> 64 1996:1:SWE:ARG:FOT:74:23 55.6700 16.4667 65 Gadus morhua
#> 65 2004:1:POL:BAL:TVL:1:1 54.5667 18.8167 61 Gadus morhua
#> 66 1993:1:SWE:ARG:GOV:85:37 54.9983 13.9913 49 Gadus morhua
#> 67 1998:1:SWE:ARG:FOT:199:8 55.0090 13.9270 48 Gadus morhua
#> 68 1995:1:DEN:DAN2:GRT:123:46 55.8166 16.6666 46 Gadus morhua
#> 69 2010:1:RUS:ATL:TVL:60:17 55.3617 18.0833 81 Gadus morhua
#> 70 2009:1:SWE:ARG:TVL:267:52 55.6688 14.5024 51 Gadus morhua
#> 71 1992:1:GFR:SOL:CHP:26021:29 56.1500 18.5333 110 Gadus morhua
#> 72 1994:4:GFR:SOL:H20:25:72 54.7167 13.1333 26 Gadus morhua
#> 73 1994:4:SWE:ARG:FOT:287:32 55.8717 16.0733 55 Gadus morhua
#> 74 2000:4:SWE:ARG:GOV:594:27 55.9362 17.0593 47 Gadus morhua
#> 75 2004:1:POL:BAL:TVL:2:2 55.2333 18.1667 61 Gadus morhua
#> 76 1998:1:RUS:ATLD:HAK:NA:37 55.5167 20.0333 82 Gadus morhua
#> 77 1995:1:DEN:DAN2:GRT:11:3 55.3166 17.2166 75 Gadus morhua
#> 78 1994:1:GFR:SOL:H20:73:34 55.6167 14.8667 76 Gadus morhua
#> 79 1996:1:DEN:DAN2:GRT:51:29 56.3450 19.6316 110 Gadus morhua
#> 80 1996:1:SWE:ARG:FOT:95:44 57.4800 17.5533 73 Gadus morhua
#> 81 2011:1:POL:BAL:TVL:26211:3 54.5267 19.3583 75 Gadus morhua
#> 82 1997:1:DEN:DAN2:GRT:18:7 55.6783 16.7883 47 Gadus morhua
#> 83 1997:4:SWE:ARG:FOT:756:26 56.2205 18.4318 72 Gadus morhua
#> 84 1991:1:LAT:90MX:LBT:000001:11 56.1167 20.5167 30 Gadus morhua
#> 85 2010:1:RUS:ATL:TVL:60:23 54.9217 19.5692 85 Gadus morhua
#> 86 1998:1:DEN:DAN2:GRT:130:61 55.6167 19.4983 87 Gadus morhua
#> 87 1995:1:RUS:RUEK:DT:NA:19 54.9833 19.6500 71 Gadus morhua
#> 88 1994:1:DEN:DAN2:GRT:63:32 55.4667 18.5333 90 Gadus morhua
#> 89 1994:1:POL:BAL:P20:Mar:18 54.9000 18.6167 50 Gadus morhua
#> 90 1996:1:DEN:DAN2:GRT:46:26 56.4433 19.9866 80 Gadus morhua
#> 91 1999:1:RUS:ATL:HAK:NA:5 54.9500 19.6333 72 Gadus morhua
#> 92 1993:1:GFR:SOL:H20:48:22 55.0667 14.2833 48 Gadus morhua
#> 93 2001:1:GFR:SOL:TVS:902:34 55.0833 14.1500 50 Gadus morhua
#> 94 2009:4:DEN:DAN2:TVL:179:16 54.9337 16.2348 45 Gadus morhua
#> 95 2006:4:DEN:DAN2:TVL:7:32 54.9951 15.3587 57 Gadus morhua
#> 96 2003:4:RUS:ATLD:TVL:NA:60 55.2083 20.1500 51 Gadus morhua
#> 97 1992:1:DEN:DAN2:GRT:79:55 55.4667 18.2167 74 Gadus morhua
#> 98 2011:1:RUS:ATLD:TVL:56:40 55.3583 19.9267 82 Gadus morhua
#> 99 2011:4:DEN:DAN2:TVL:39:42 55.7076 14.7245 53 Gadus morhua
#> 100 1996:1:GFR:SOL:H20:47:51 55.1333 14.2667 49 Gadus morhua
#> CPUEun a b length_cm length_cm2 weight_kg CPUEun_kg
#> 1 1.000000 0.007345618 3.082202 335 335 445.37563 445.37563
#> 2 1.000000 0.007345618 3.082202 285 285 270.61760 270.61760
#> 3 2.000000 0.007345618 3.082202 225 225 130.59602 261.19205
#> 4 1.000000 0.007345618 3.082202 220 220 121.85635 121.85635
#> 5 1.000000 0.007345618 3.082202 215 215 113.52063 113.52063
#> 6 1.000000 0.007345618 3.082202 180 180 65.64985 65.64985
#> 7 1.000000 0.007345618 3.082202 175 175 60.19004 60.19004
#> 8 1.000000 0.007345618 3.082202 160 160 45.66372 45.66372
#> 9 2.000000 0.007345618 3.082202 150 150 37.42667 74.85333
#> 10 3.000000 0.007345618 3.082202 145 145 33.71329 101.13986
#> 11 1.000000 0.007345618 3.082202 140 140 30.25718 30.25718
#> 12 1.000000 0.008367352 3.055881 136 136 27.69677 27.69677
#> 13 1.000000 0.006985661 3.112176 127 127 24.63852 24.63852
#> 14 2.000000 0.007345618 3.082202 130 130 24.07846 48.15692
#> 15 1.000000 0.008339286 3.052078 127 127 21.98379 21.98379
#> 16 1.000000 0.008367352 3.055881 123 123 20.37462 20.37462
#> 17 2.000000 0.007345618 3.082202 121 121 19.30161 38.60322
#> 18 2.000000 0.006985661 3.112176 116 116 18.58505 37.17010
#> 19 2.000000 0.007444717 3.070310 121 121 18.47755 36.95509
#> 20 2.000000 0.006436894 3.144784 112 112 17.90706 35.81411
#> 21 1.000000 0.008339286 3.052078 118 118 17.56609 17.56609
#> 22 1.000000 0.008367352 3.055881 117 117 17.48711 17.48711
#> 23 2.000000 0.007554303 3.071838 118 118 17.48561 34.97121
#> 24 2.000000 0.007243730 3.076256 118 118 17.12383 34.24765
#> 25 1.000000 0.007345618 3.082202 116 116 16.94747 16.94747
#> 26 1.000000 0.006436894 3.144784 110 110 16.92058 16.92058
#> 27 2.000000 0.007243730 3.076256 117 117 16.68132 33.36265
#> 28 2.000000 0.007554303 3.071838 116 116 16.59110 33.18221
#> 29 2.000000 0.007554303 3.071838 115 115 16.15566 32.31132
#> 30 1.000000 0.006436894 3.144784 108 108 15.97183 15.97183
#> 31 1.000000 0.008367352 3.055881 113 113 15.72359 15.72359
#> 32 2.000000 0.008367352 3.055881 113 113 15.72359 31.44717
#> 33 2.000000 0.007303857 3.095567 111 111 15.66706 31.33412
#> 34 2.000000 0.006830676 3.097913 113 113 15.65748 31.31495
#> 35 2.000000 0.007345618 3.082202 113 113 15.63258 31.26517
#> 36 1.000000 0.006985661 3.112176 109 109 15.31218 15.31218
#> 37 1.000000 0.008367352 3.055881 112 112 15.30223 15.30223
#> 38 2.000000 0.009208543 3.001584 118 118 15.24467 30.48935
#> 39 1.000000 0.007303857 3.095567 110 110 15.23425 15.23425
#> 40 2.000000 0.009197813 2.994628 119 119 15.10690 30.21379
#> 41 1.000000 0.007017543 3.107987 109 109 15.08273 15.08273
#> 42 1.000000 0.006436894 3.144784 106 106 15.06002 15.06002
#> 43 1.000000 0.008339286 3.052078 112 112 14.97969 14.97969
#> 44 2.000000 0.007345618 3.082202 111 111 14.79541 29.59081
#> 45 2.000000 0.008339286 3.052078 111 111 14.57521 29.15042
#> 46 2.000000 0.008377248 3.027632 115 115 14.52564 29.05128
#> 47 1.000000 0.006985661 3.112176 107 107 14.45462 14.45462
#> 48 2.000000 0.007693063 3.064660 111 111 14.26658 28.53316
#> 49 1.000000 0.006436894 3.144784 104 104 14.18438 14.18438
#> 50 1.000000 0.006985661 3.112176 106 106 14.03833 14.03833
#> 51 1.000000 0.006985661 3.112176 106 106 14.03833 14.03833
#> 52 3.000000 0.006985661 3.112176 106 106 14.03833 42.11499
#> 53 2.000000 0.009197813 2.994628 116 116 13.99484 27.98968
#> 54 3.000000 0.007345618 3.082202 109 109 13.98905 41.96716
#> 55 2.000000 0.007345618 3.082202 109 109 13.98905 27.97811
#> 56 2.000000 0.007746254 3.044723 113 113 13.80850 27.61701
#> 57 1.000000 0.006436894 3.144784 103 103 13.75987 13.75987
#> 58 1.935484 0.007554303 3.071838 109 109 13.70373 26.52336
#> 59 2.000000 0.008367352 3.055881 108 108 13.69270 27.38541
#> 60 1.000000 0.007303857 3.095567 106 106 13.58386 13.58386
#> 61 2.000000 0.007920229 3.035684 113 113 13.52806 27.05613
#> 62 1.000000 0.007554303 3.071838 108 108 13.32119 13.32119
#> 63 1.000000 0.008367352 3.055881 107 107 13.30894 13.30894
#> 64 2.000000 0.008367352 3.055881 107 107 13.30894 26.61788
#> 65 2.000000 0.007991791 3.046753 110 110 13.25143 26.50287
#> 66 1.000000 0.006985661 3.112176 104 104 13.23031 13.23031
#> 67 2.000000 0.007345618 3.082202 107 107 13.21293 26.42585
#> 68 1.000000 0.007303857 3.095567 105 105 13.19107 13.19107
#> 69 2.000000 0.007920229 3.035684 112 112 13.16790 26.33580
#> 70 2.000000 0.007746254 3.044723 111 111 13.07776 26.15552
#> 71 1.000000 0.007017543 3.107987 104 104 13.03462 13.03462
#> 72 2.000000 0.008339286 3.052078 107 107 13.03068 26.06136
#> 73 2.000000 0.008339286 3.052078 107 107 13.03068 26.06136
#> 74 2.000000 0.007554303 3.071838 107 107 12.94592 25.89184
#> 75 2.000000 0.007991791 3.046753 109 109 12.88780 25.77560
#> 76 2.000000 0.007345618 3.082202 106 106 12.83601 25.67202
#> 77 1.000000 0.007303857 3.095567 104 104 12.80604 12.80604
#> 78 2.000000 0.008339286 3.052078 106 106 12.66255 25.32509
#> 79 1.000000 0.008367352 3.055881 105 105 12.56325 12.56325
#> 80 2.000000 0.008367352 3.055881 105 105 12.56325 25.12651
#> 81 2.000000 0.007917341 3.037179 110 110 12.55028 25.10056
#> 82 1.000000 0.008046923 3.063870 105 105 12.53982 12.53982
#> 83 2.000000 0.008046923 3.063870 105 105 12.53982 25.07964
#> 84 2.000000 0.006436894 3.144784 100 100 12.53846 25.07692
#> 85 16.000000 0.007920229 3.035684 110 110 12.46698 199.47172
#> 86 1.000000 0.007345618 3.082202 105 105 12.46643 12.46643
#> 87 2.000000 0.007303857 3.095567 103 103 12.42870 24.85740
#> 88 1.000000 0.008339286 3.052078 105 105 12.30147 12.30147
#> 89 2.000000 0.008339286 3.052078 105 105 12.30147 24.60294
#> 90 1.000000 0.008367352 3.055881 104 104 12.20119 12.20119
#> 91 2.000000 0.006830676 3.097913 104 104 12.10758 24.21515
#> 92 2.000000 0.006985661 3.112176 101 101 12.07836 24.15672
#> 93 2.000000 0.007693063 3.064660 105 105 12.03257 24.06513
#> 94 2.000000 0.007746254 3.044723 108 108 12.03105 24.06210
#> 95 1.935484 0.009208543 3.001584 109 109 12.01426 23.25341
#> 96 2.000000 0.007444717 3.070310 105 105 11.95433 23.90866
#> 97 1.000000 0.007017543 3.107987 101 101 11.90117 11.90117
#> 98 2.000000 0.007917341 3.037179 108 108 11.86999 23.73998
#> 99 2.068966 0.007917341 3.037179 108 108 11.86999 24.55860
#> 100 4.000000 0.008367352 3.055881 103 103 11.84620 47.38482
hlcodL <- hlcodL %>% filter(weight_kg < 100)
ggplot(hlcodL, aes(weight_kg, length_cm2)) +
geom_point() +
facet_wrap(~Year)
# Now do the same for flounder
# First standardize length to cm and then check how zero-catches are implemented at this stage
hlfleL <- hlfleL %>%
mutate(length_cm = ifelse(LngtCode %in% c(".", "0"),
LngtClass/10,
LngtClass)) # Standardize length (https://vocab.ices.dk/?ref=18)
filter(hlfleL, length_cm == 0) # No such thing
#> # A tibble: 0 × 50
#> # … with 50 variables: RecordType <chr>, Survey <chr>, Quarter <int>,
#> # Country <chr>, Ship <chr>, Gear <chr>, SweepLngt <int>, GearEx <chr>,
#> # DoorType <lgl>, StNo <chr>, HaulNo <int>, Year <int>, SpecCodeType <chr>,
#> # SpecCode <int>, SpecVal <fct>, Sex <chr>, TotalNo <dbl>,
#> # CatIdentifier <int>, NoMeas <int>, SubFactor <dbl>, SubWgt <int>,
#> # CatCatchWgt <int>, LngtCode <chr>, LngtClass <int>, HLNoAtLngt <dbl>,
#> # DevStage <chr>, LenMeasType <int>, DateofCalculation <int>, …
bits_ca_fle <- bits_ca_fle %>%
drop_na(IndWgt) %>%
drop_na(LngtClass) %>%
filter(IndWgt > 0 & LngtClass > 0) %>% # Filter positive length and weight
mutate(weight_kg = IndWgt/1000) %>%
mutate(length_cm = ifelse(LngtCode == ".",
LngtClass/10,
LngtClass)) %>% # Standardize length ((https://vocab.ices.dk/?ref=18))
mutate(keep = ifelse(LngtCode == "." & Year == 2008, "N", "Y")) %>%
filter(keep == "Y") %>%
filter(length_cm < 70)
# Now check if all rows where length is NA are the ones with zero catch!
hlfleL %>%
mutate(length2 = replace_na(length_cm, -9),
no_length = ifelse(length2 < 0, "T", "F")) %>%
ggplot(., aes(length2, CPUEun, color = no_length)) + geom_point(alpha = 0.2) + facet_wrap(~no_length)
#> Warning: Removed 42 rows containing missing values (geom_point).
hlfleL %>% mutate(length2 = replace_na(length_cm, -9)) %>% group_by(length2) %>% distinct(CPUEun) %>% arrange(CPUEun)
#> # A tibble: 12,467 × 2
#> # Groups: length2 [253]
#> CPUEun length2
#> <dbl> <dbl>
#> 1 0 -9
#> 2 0.667 19
#> 3 0.667 21
#> 4 0.667 35
#> 5 0.667 39
#> 6 0.667 40
#> 7 0.667 42
#> 8 0.870 27
#> 9 0.870 32
#> 10 0.870 37.5
#> # … with 12,457 more rows
# Right, so all hauls with zero catch have NA length_cm. I don't have any NA catches
t <- hlfleL %>% drop_na(CPUEun)
# Well, 11 rows. I will remove them
hlfleL <- hlfleL %>% drop_na(CPUEun)
t <- hlfleL %>% filter(CPUEun == 0)
t <- hlfleL %>% drop_na(length_cm)
# In other words, a zero catch is when the catch is zero and length_cm is NA
# In order to not get any NA CPUEs in unit biomass because length is NA (I want them instead
# to be 0, as the numbers-CPUE is), I will replace length_cm == NA with length_cm == 0 before
# calculating biomass cpue
hlfleL <- hlfleL %>% mutate(length_cm2 = replace_na(length_cm, 0))
# Standardize length in the haul-data and calculate weight
hlfleL <- hlfleL %>%
mutate(weight_kg = (a*length_cm2^b)/1000) %>%
mutate(CPUEun_kg = weight_kg*CPUEun)
# Plot and check it's correct also in this data
ggplot(hlfleL, aes(weight_kg, length_cm2)) +
geom_point() +
facet_wrap(~Year)
#> Warning: Removed 3311 rows containing missing values (geom_point).
# Check
t <- hlfleL %>% drop_na(CPUEun_kg) # Should not have any NA in biomass-catch
t <- hlfleL %>% filter(CPUEun_kg == 0) # Should result in a few percent of rows (note this is not proportion of hauls, but rows)
t <- hlfleL %>% drop_na(length_cm2) # Should be no NA
# What is the proportion of zero-catch hauls?
cod_0plot <- hlcodL %>%
group_by(haul.id, Year, Quarter) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
group_by(Year, Quarter, zero_catch) %>%
summarise(n = n()) %>%
ungroup() %>%
pivot_wider(names_from = zero_catch, values_from = n) %>%
mutate(prop_zero_catch_hauls = Y/(N+Y)) %>%
ggplot(., aes(Year, prop_zero_catch_hauls)) + geom_bar(stat = "identity") +
coord_cartesian(expand = 0, ylim = c(0, 1)) +
facet_wrap(~ Quarter) +
ggtitle("Cod")
# How many zero-catch hauls?
fle_0plot <- hlfleL %>%
group_by(haul.id, Year, Quarter) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
group_by(Year, Quarter, zero_catch) %>%
summarise(n = n()) %>%
ungroup() %>%
pivot_wider(names_from = zero_catch, values_from = n) %>%
mutate(prop_zero_catch_hauls = Y/(N+Y)) %>%
ggplot(., aes(Year, prop_zero_catch_hauls)) + geom_bar(stat = "identity") +
coord_cartesian(expand = 0, ylim = c(0, 1)) +
facet_wrap(~ Quarter) +
ggtitle("Flounder")
cod_0plot / fle_0plot
#> Warning: Removed 1 rows containing missing values (position_stack).
#> Warning: Removed 1 rows containing missing values (position_stack).
Standardize according to Orio
To get unit: kg of fish caught by trawling for 1 h a standard bottom swept area of 0.45km2 using a TVL trawl with 75 m sweeps at the standard speed of three knots
# Remove hauls done with the TVL gear with a SweepLngt < 50 (these are calibration hauls, pers. com. Anders & Ale)
# And also hauls without length-information
# Remove pelagic gear
hlcodL <- hlcodL %>%
mutate(SweepLngt2 = replace_na(SweepLngt, 50)) %>%
mutate(keep = ifelse(Gear == "TVL" & SweepLngt2 < 50, "N", "Y")) %>%
filter(keep == "Y") %>%
dplyr::select(-keep, -SweepLngt2) %>%
filter(!Gear == "PEL")
hlfleL <- hlfleL %>%
mutate(SweepLngt2 = replace_na(SweepLngt, 50)) %>%
mutate(keep = ifelse(Gear == "TVL" & SweepLngt2 < 50, "N", "Y")) %>%
filter(keep == "Y") %>%
dplyr::select(-keep, -SweepLngt2) %>%
filter(!Gear == "PEL")
# Add in RS and RSA-values from the sweep file
# CPUE should be multiplied with RS and RSA to standardize to a relative speed and gear dimension.
# There is not a single file will all RS and RSA values. Instead they come in three files:
# - sweep (non-Swedish hauls between 1991-2016)
# - + calculated based on trawl speed and gear dimensions.
# I will join in the RS and RSA values from all sources, then standardize and filter
# away non-standardized hauls
# sort(unique(sweep$Year))
# sort(unique(sweep$Country))
# Since I don't have the sweep data for Swedish data, I have to calculate it from scratch using the
# equation in Orio's spreadsheet
# First I will join in the sweep data,
sweep_sel <- sweep %>% rename("haul.id" = "ï..haul.id") %>% dplyr::select(haul.id, RSA, RS)
hlcodL2 <- left_join(hlcodL, sweep_sel)
hlfleL2 <- left_join(hlfleL, sweep_sel)
hlcodL2 <- hlcodL2 %>%
rename("RS_sweep" = "RS",
"RSA_sweep" = "RSA") %>%
mutate(RS_sweep = as.numeric(RS_sweep),
RSA_sweep = as.numeric(RSA_sweep))
hlfleL2 <- hlfleL2 %>%
rename("RS_sweep" = "RS",
"RSA_sweep" = "RSA") %>%
mutate(RS_sweep = as.numeric(RS_sweep),
RSA_sweep = as.numeric(RSA_sweep))
sort(colnames(hlcodL2))
#> [1] "a" "b" "BySpecRecCode"
#> [4] "CatCatchWgt" "CatIdentifier" "Country"
#> [7] "CPUEun" "CPUEun_kg" "DataType"
#> [10] "DateofCalculation" "Depth" "DevStage"
#> [13] "DoorType" "Fishing.line" "Gear"
#> [16] "GearEx" "GroundSpeed" "haul.id"
#> [19] "HaulDur" "HaulNo" "HaulVal"
#> [22] "HLNoAtLngt" "IDx" "length_cm"
#> [25] "length_cm2" "LenMeasType" "LngtClass"
#> [28] "LngtCode" "NoMeas" "Quarter"
#> [31] "RecordType" "Rect" "RS_sweep"
#> [34] "RSA_sweep" "Sex" "Ship"
#> [37] "Ship2" "Ship3" "ShootLat"
#> [40] "ShootLong" "SpecCode" "SpecCodeType"
#> [43] "Species" "SpecVal" "StdSpecRecCode"
#> [46] "StNo" "sub_div" "SubFactor"
#> [49] "SubWgt" "Survey" "SweepLngt"
#> [52] "TotalNo" "Valid_Aphia" "weight_kg"
#> [55] "Year"
sort(colnames(hlfleL2))
#> [1] "a" "b" "BySpecRecCode"
#> [4] "CatCatchWgt" "CatIdentifier" "Country"
#> [7] "CPUEun" "CPUEun_kg" "DataType"
#> [10] "DateofCalculation" "Depth" "DevStage"
#> [13] "DoorType" "Fishing.line" "Gear"
#> [16] "GearEx" "GroundSpeed" "haul.id"
#> [19] "HaulDur" "HaulNo" "HaulVal"
#> [22] "HLNoAtLngt" "IDx" "length_cm"
#> [25] "length_cm2" "LenMeasType" "LngtClass"
#> [28] "LngtCode" "NoMeas" "Quarter"
#> [31] "RecordType" "Rect" "RS_sweep"
#> [34] "RSA_sweep" "Sex" "Ship"
#> [37] "Ship2" "Ship3" "ShootLat"
#> [40] "ShootLong" "SpecCode" "SpecCodeType"
#> [43] "Species" "SpecVal" "StdSpecRecCode"
#> [46] "StNo" "sub_div" "SubFactor"
#> [49] "SubWgt" "Survey" "SweepLngt"
#> [52] "TotalNo" "Valid_Aphia" "weight_kg"
#> [55] "Year"
# I will calculate a RS and RSA column in the catch data based on Ale's equation in the sweep file:
sort(unique(hlcodL2$GroundSpeed))
#> [1] -9.0 0.1 0.2 0.8 1.7 1.8 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8
#> [16] 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3
#> [31] 4.4 4.5 4.6 4.7 4.9 5.0 5.2 5.3 5.4 5.5 5.6 5.7 5.9 6.0 6.1
#> [46] 6.2 6.3 6.6 6.7 6.8 6.9 7.1 7.3 8.6
sort(unique(hlcodL2$Fishing.line))
#> [1] -9.00 16.54 28.00 33.22 35.20 36.00 39.80 63.46 83.00 160.00
sort(unique(hlcodL2$SweepLngt))
#> [1] 0 40 50 60 75 87 90 95 100 110 125 135 180 182 185 188 200 203 225
#> [20] 235
# First replace -9 in the columns I use for the calculations with NA so I don't end up with real numbers that are wrong!
hlcodL2 <- hlcodL2 %>% mutate(GroundSpeed = ifelse(GroundSpeed == -9, NA, GroundSpeed),
Fishing.line = ifelse(Fishing.line == -9, NA, Fishing.line),
SweepLngt = ifelse(SweepLngt == -9, NA, SweepLngt))
hlfleL2 <- hlfleL2 %>% mutate(GroundSpeed = ifelse(GroundSpeed == -9, NA, GroundSpeed),
Fishing.line = ifelse(Fishing.line == -9, NA, Fishing.line),
SweepLngt = ifelse(SweepLngt == -9, NA, SweepLngt))
hlcodL2 %>% filter(Quarter == 1) %>%
distinct(GroundSpeed, Fishing.line, SweepLngt) %>% as.data.frame()
#> SweepLngt Fishing.line GroundSpeed
#> 1 NA NA NA
#> 2 NA 35.20 3.5
#> 3 NA 28.00 NA
#> 4 NA 35.20 3.6
#> 5 NA 35.20 3.4
#> 6 NA 36.00 NA
#> 7 110 NA NA
#> 8 NA 39.80 NA
#> 9 60 NA NA
#> 10 NA 35.20 NA
#> 11 NA 35.20 3.0
#> 12 NA 35.20 3.1
#> 13 NA 35.20 3.2
#> 14 NA 36.00 3.0
#> 15 50 160.00 3.3
#> 16 50 160.00 3.2
#> 17 50 160.00 3.0
#> 18 50 160.00 3.1
#> 19 50 160.00 3.4
#> 20 100 160.00 3.1
#> 21 50 160.00 2.8
#> 22 50 160.00 2.7
#> 23 185 83.00 3.1
#> 24 125 83.00 3.1
#> 25 185 83.00 3.2
#> 26 185 83.00 3.3
#> 27 50 160.00 2.9
#> 28 NA 36.00 3.8
#> 29 NA 36.00 3.6
#> 30 NA 36.00 4.0
#> 31 NA 36.00 4.6
#> 32 NA 36.00 3.4
#> 33 NA 36.00 4.2
#> 34 NA 36.00 3.2
#> 35 NA 36.00 2.6
#> 36 NA 36.00 2.8
#> 37 50 160.00 2.6
#> 38 185 83.00 3.0
#> 39 185 83.00 2.8
#> 40 185 83.00 3.4
#> 41 185 83.00 3.5
#> 42 185 83.00 2.7
#> 43 185 83.00 2.9
#> 44 NA 36.00 4.4
#> 45 NA 36.00 2.4
#> 46 180 83.00 3.1
#> 47 180 83.00 3.2
#> 48 180 83.00 3.0
#> 49 180 83.00 3.3
#> 50 180 83.00 3.4
#> 51 180 83.00 3.5
#> 52 180 83.00 2.8
#> 53 180 83.00 3.9
#> 54 NA 36.00 3.7
#> 55 NA 36.00 3.1
#> 56 NA NA 3.2
#> 57 NA NA 3.3
#> 58 NA NA 3.6
#> 59 NA NA 3.4
#> 60 90 160.00 3.4
#> 61 NA NA 3.0
#> 62 NA NA 3.8
#> 63 NA NA 3.9
#> 64 NA NA 3.7
#> 65 90 160.00 3.2
#> 66 90 160.00 3.8
#> 67 90 160.00 3.7
#> 68 90 160.00 3.5
#> 69 90 160.00 3.6
#> 70 90 160.00 3.0
#> 71 90 160.00 3.9
#> 72 NA NA 4.3
#> 73 NA NA 3.1
#> 74 NA NA 4.9
#> 75 NA NA 4.0
#> 76 NA NA 4.6
#> 77 NA NA 2.9
#> 78 NA NA 4.2
#> 79 NA NA 3.5
#> 80 NA NA 4.7
#> 81 203 83.00 3.8
#> 82 203 83.00 3.5
#> 83 203 83.00 3.9
#> 84 203 83.00 3.7
#> 85 203 83.00 4.0
#> 86 NA NA 2.8
#> 87 203 83.00 3.6
#> 88 203 83.00 3.4
#> 89 NA NA 2.6
#> 90 NA NA 2.7
#> 91 NA NA 2.4
#> 92 NA NA 4.1
#> 93 235 83.00 3.7
#> 94 235 83.00 4.1
#> 95 235 83.00 3.9
#> 96 235 83.00 3.8
#> 97 235 83.00 3.0
#> 98 235 83.00 3.6
#> 99 235 83.00 4.0
#> 100 235 83.00 3.1
#> 101 235 83.00 3.5
#> 102 135 83.00 3.9
#> 103 135 83.00 3.7
#> 104 135 83.00 3.5
#> 105 135 83.00 3.6
#> 106 135 83.00 3.2
#> 107 225 83.00 3.5
#> 108 225 83.00 3.4
#> 109 225 83.00 2.7
#> 110 225 83.00 3.3
#> 111 225 83.00 3.6
#> 112 225 83.00 3.2
#> 113 225 83.00 3.7
#> 114 0 83.00 3.6
#> 115 225 83.00 3.1
#> 116 NA 63.46 NA
#> 117 NA NA 4.4
#> 118 NA 36.00 1.7
#> 119 203 83.00 3.3
#> 120 NA 36.00 3.9
#> 121 75 63.46 3.1
#> 122 75 63.46 3.2
#> 123 75 63.46 3.0
#> 124 75 63.46 2.6
#> 125 75 63.46 2.9
#> 126 235 83.00 3.2
#> 127 235 83.00 3.3
#> 128 235 83.00 3.4
#> 129 100 160.00 3.6
#> 130 100 160.00 3.8
#> 131 100 160.00 3.5
#> 132 100 160.00 3.7
#> 133 100 160.00 3.4
#> 134 100 160.00 3.3
#> 135 NA 33.22 3.1
#> 136 NA 33.22 3.3
#> 137 NA 33.22 3.0
#> 138 NA 33.22 3.2
#> 139 NA 33.22 3.4
#> 140 NA 63.46 3.1
#> 141 NA 63.46 3.0
#> 142 NA 63.46 2.8
#> 143 NA 63.46 2.9
#> 144 75 63.46 2.7
#> 145 75 63.46 2.8
#> 146 NA 63.46 3.2
#> 147 75 33.22 NA
#> 148 NA 63.46 3.3
#> 149 NA 63.46 3.4
#> 150 75 63.46 3.3
#> 151 NA 33.22 2.9
#> 152 75 63.46 2.4
#> 153 NA 33.22 2.8
#> 154 60 63.46 3.0
#> 155 60 63.46 2.9
#> 156 60 63.46 3.1
#> 157 60 63.46 2.8
#> 158 NA 63.46 2.5
#> 159 NA 63.46 2.7
#> 160 95 63.46 3.1
#> 161 95 63.46 3.0
#> 162 NA 33.22 NA
#> 163 95 63.46 NA
#> 164 NA 33.22 3.5
#> 165 NA 33.22 3.6
#> 166 75 63.46 NA
#> 167 95 63.46 2.9
#> 168 NA 63.46 3.7
#> 169 NA 63.46 2.2
#> 170 NA 33.22 3.8
#> 171 NA 63.46 2.3
#> 172 NA 63.46 2.6
#> 173 NA 33.22 3.7
#> 174 NA 33.22 2.7
#> 175 NA 63.46 2.0
#> 176 NA 63.46 0.1
#> 177 NA 63.46 2.1
#> 178 NA 63.46 1.7
#> 179 NA 63.46 2.4
#> 180 NA 33.22 2.6
#> 181 NA 63.46 3.9
#> 182 NA 33.22 2.3
#> 183 200 33.22 2.9
#> 184 NA 63.46 8.6
#> 185 75 63.46 3.4
#> 186 NA 63.46 0.2
#> 187 75 63.46 3.6
#> 188 75 63.46 3.5
#> 189 75 63.46 2.5
#> 190 NA 16.54 3.4
#> 191 185 83.00 2.6
#> 192 50 160.00 3.6
#> 193 203 83.00 3.2
#> 194 110 160.00 3.6
#> 195 110 160.00 3.1
#> 196 110 160.00 3.4
#> 197 NA 63.46 3.6
#> 198 NA 63.46 3.8
#> 199 NA 63.46 4.2
#> 200 NA 63.46 5.5
#> 201 NA 63.46 5.2
#> 202 NA 63.46 6.6
#> 203 NA 63.46 5.0
#> 204 NA 63.46 6.1
#> 205 NA 63.46 6.2
#> 206 NA 63.46 6.0
#> 207 NA 63.46 5.9
#> 208 NA 63.46 6.8
#> 209 NA 63.46 4.1
#> 210 NA 63.46 4.3
hlcodL2 %>% filter(Quarter == 4) %>%
distinct(GroundSpeed, Fishing.line, SweepLngt) %>% as.data.frame()
#> SweepLngt Fishing.line GroundSpeed
#> 1 NA 36.00 NA
#> 2 NA 36.00 3.4
#> 3 NA 36.00 3.6
#> 4 NA 36.00 3.2
#> 5 NA 36.00 3.8
#> 6 NA 36.00 4.0
#> 7 180 83.00 2.9
#> 8 180 83.00 2.8
#> 9 180 83.00 3.1
#> 10 NA 36.00 4.2
#> 11 180 83.00 3.2
#> 12 180 83.00 3.3
#> 13 180 83.00 3.0
#> 14 NA 36.00 3.0
#> 15 185 83.00 2.8
#> 16 185 83.00 3.0
#> 17 185 83.00 3.2
#> 18 185 83.00 3.3
#> 19 185 83.00 3.1
#> 20 185 83.00 3.5
#> 21 185 83.00 3.4
#> 22 185 83.00 3.6
#> 23 203 83.00 3.6
#> 24 203 83.00 3.7
#> 25 203 83.00 3.5
#> 26 203 83.00 3.8
#> 27 203 83.00 3.9
#> 28 203 83.00 3.4
#> 29 100 160.00 3.4
#> 30 100 83.00 3.5
#> 31 100 83.00 3.4
#> 32 225 83.00 3.6
#> 33 225 83.00 3.5
#> 34 100 83.00 4.0
#> 35 100 160.00 3.5
#> 36 100 83.00 3.7
#> 37 100 83.00 3.3
#> 38 100 83.00 3.2
#> 39 100 83.00 3.6
#> 40 225 83.00 3.3
#> 41 225 83.00 3.4
#> 42 180 83.00 3.6
#> 43 NA 28.00 NA
#> 44 225 83.00 3.2
#> 45 180 83.00 3.4
#> 46 180 83.00 3.5
#> 47 225 83.00 3.7
#> 48 225 83.00 3.8
#> 49 185 83.00 3.8
#> 50 225 83.00 3.9
#> 51 185 83.00 3.7
#> 52 NA 36.00 1.7
#> 53 NA 63.46 NA
#> 54 40 160.00 3.7
#> 55 100 160.00 3.1
#> 56 100 160.00 3.3
#> 57 225 83.00 3.0
#> 58 40 160.00 3.4
#> 59 100 160.00 3.2
#> 60 100 160.00 3.7
#> 61 NA NA 3.2
#> 62 NA 63.46 2.9
#> 63 NA 63.46 3.0
#> 64 NA NA 4.1
#> 65 75 63.46 3.1
#> 66 75 63.46 3.4
#> 67 75 63.46 2.9
#> 68 NA 63.46 3.1
#> 69 NA 63.46 2.8
#> 70 75 63.46 NA
#> 71 75 63.46 2.8
#> 72 75 63.46 3.7
#> 73 NA 63.46 3.2
#> 74 NA NA NA
#> 75 75 63.46 2.7
#> 76 75 160.00 3.5
#> 77 75 160.00 3.4
#> 78 75 160.00 3.3
#> 79 75 160.00 3.7
#> 80 75 160.00 3.8
#> 81 75 160.00 3.2
#> 82 NA 33.22 3.0
#> 83 75 160.00 3.6
#> 84 NA 33.22 3.2
#> 85 NA 33.22 2.8
#> 86 NA 33.22 3.4
#> 87 NA 33.22 NA
#> 88 NA 63.46 2.7
#> 89 NA 33.22 3.6
#> 90 75 63.46 3.0
#> 91 NA 33.22 3.1
#> 92 NA 33.22 3.3
#> 93 NA 33.22 2.9
#> 94 75 63.46 3.2
#> 95 75 63.46 2.5
#> 96 NA 33.22 3.7
#> 97 NA 33.22 3.5
#> 98 75 63.46 3.3
#> 99 50 63.46 3.0
#> 100 NA 63.46 2.5
#> 101 75 33.22 NA
#> 102 NA 63.46 3.3
#> 103 50 63.46 2.9
#> 104 50 63.46 3.3
#> 105 95 63.46 2.9
#> 106 95 63.46 3.0
#> 107 95 63.46 2.6
#> 108 87 63.46 2.9
#> 109 87 63.46 3.0
#> 110 NA 63.46 3.6
#> 111 NA 63.46 3.5
#> 112 NA 63.46 3.4
#> 113 NA 33.22 1.8
#> 114 NA 63.46 2.6
#> 115 95 63.46 2.8
#> 116 NA 63.46 4.1
#> 117 95 63.46 3.1
#> 118 NA 33.22 2.5
#> 119 NA 33.22 2.7
#> 120 NA 33.22 2.6
#> 121 NA 63.46 2.1
#> 122 NA 63.46 2.0
#> 123 NA 63.46 1.7
#> 124 95 63.46 3.2
#> 125 NA 33.22 3.9
#> 126 75 63.46 2.6
#> 127 NA 33.22 4.9
#> 128 75 63.46 2.4
#> 129 188 83.00 3.3
#> 130 180 83.00 3.7
#> 131 185 83.00 3.9
#> 132 182 83.00 3.3
#> 133 188 83.00 3.2
#> 134 188 83.00 3.6
#> 135 180 83.00 3.8
#> 136 188 83.00 3.4
#> 137 188 83.00 3.9
#> 138 188 83.00 3.7
#> 139 182 83.00 3.7
#> 140 50 160.00 3.0
#> 141 50 160.00 3.2
#> 142 50 160.00 2.9
#> 143 50 160.00 3.3
#> 144 50 160.00 3.1
#> 145 50 160.00 3.8
#> 146 50 160.00 2.8
#> 147 50 160.00 2.7
#> 148 50 63.46 NA
#> 149 50 63.46 3.1
#> 150 NA 33.22 2.1
#> 151 NA 63.46 4.5
#> 152 NA 63.46 3.8
#> 153 NA 63.46 5.4
#> 154 NA 63.46 4.2
#> 155 NA 63.46 4.3
#> 156 NA 63.46 4.6
#> 157 NA 63.46 3.9
#> 158 NA 63.46 3.7
#> 159 NA 63.46 2.2
#> 160 NA 63.46 4.4
#> 161 NA 63.46 4.7
#> 162 NA 63.46 5.6
#> 163 NA 63.46 6.3
#> 164 NA 63.46 5.2
#> 165 NA 63.46 6.0
#> 166 NA 63.46 5.3
#> 167 NA 63.46 6.9
#> 168 NA 63.46 6.2
#> 169 NA 63.46 5.9
#> 170 NA 63.46 7.1
#> 171 NA 63.46 7.3
#> 172 NA 63.46 6.7
#> 173 NA 63.46 0.8
#> 174 NA 63.46 4.9
#> 175 NA 63.46 5.7
#> 176 75 63.46 3.5
# Hmm, Q1 has at least one of the RS or RSA variables as NAs. Will be difficult to standardize!
# Hope the correction factors are present in Ales conversion data
# Now calculate correction factors
hlcodL2 <- hlcodL2 %>% mutate(RS_x = 3/GroundSpeed,
Horizontal.opening..m. = Fishing.line*0.67,
Swep.one.side..after.formula...meter = 0.258819045*SweepLngt, # SIN(RADIANS(15))
Size.final..m = Horizontal.opening..m. + (Swep.one.side..after.formula...meter*2),
Swept.area = (Size.final..m*3*1860)/1000000,
RSA_x = 0.45388309675081/Swept.area)
hlfleL2 <- hlfleL2 %>% mutate(RS_x = 3/GroundSpeed,
Horizontal.opening..m. = Fishing.line*0.67,
Swep.one.side..after.formula...meter = 0.258819045*SweepLngt, # SIN(RADIANS(15))
Size.final..m = Horizontal.opening..m. + (Swep.one.side..after.formula...meter*2),
Swept.area = (Size.final..m*3*1860)/1000000,
RSA_x = 0.45388309675081/Swept.area)
# Check EQ. is correct by recalculating it in the sweep file
sweep <- sweep %>% mutate(Horizontal.opening..m.2 = Fishing.line*0.67,
Swep.one.side..after.formula...meter2 = 0.258819045*SweepLngt, # SIN(RADIANS(15))
Size.final..m2 = Horizontal.opening..m.2 + (Swep.one.side..after.formula...meter2*2),
Swept.area2 = (Size.final..m2*3*1860)/1000000,
RSA_x = 0.45388309675081/Swept.area2)
sweep %>%
drop_na() %>%
ggplot(., aes(as.numeric(RSA), RSA_x)) + geom_point() + geom_abline(intercept = 0, slope = 1)
# Yes it's the same
# Replace NAs with -1/3 (because ICES codes missing values as -9 and in the calculation above they get -1/3),
# so that I can filter them easily later
# sort(unique(hlcodL2$RS_x))
# sort(unique(hlcodL2$RSA_x))
hlcodL2$RS_x[is.na(hlcodL2$RS_x)] <- -1/3
hlcodL2$RS_sweep[is.na(hlcodL2$RS_sweep)] <- -1/3
hlcodL2$RSA_x[is.na(hlcodL2$RSA_x)] <- -1/3
hlcodL2$RSA_sweep[is.na(hlcodL2$RSA_sweep)] <- -1/3
hlfleL2$RS_x[is.na(hlfleL2$RS_x)] <- -1/3
hlfleL2$RS_sweep[is.na(hlfleL2$RS_sweep)] <- -1/3
hlfleL2$RSA_x[is.na(hlfleL2$RSA_x)] <- -1/3
hlfleL2$RSA_sweep[is.na(hlfleL2$RSA_sweep)] <- -1/3
# Compare the difference correction factors (calculated vs imported from sweep file)
p1 <- ggplot(filter(hlcodL2, RS_x > 0), aes(RS_x)) + geom_histogram() + xlim(0.4, 1.76)
p2 <- ggplot(hlcodL2, aes(RSA_x)) + geom_histogram()
p3 <- ggplot(hlcodL2, aes(RS_sweep)) + geom_histogram()
p4 <- ggplot(hlcodL2, aes(RSA_sweep)) + geom_histogram()
(p1 + p2) / (p3 + p4)
#> Warning: Removed 284 rows containing non-finite values (stat_bin).
#> Warning: Removed 2 rows containing missing values (geom_bar).
p5 <- ggplot(filter(hlfleL2, RS_x > 0), aes(RS_x)) + geom_histogram() + xlim(0.4, 1.76)
p6 <- ggplot(hlfleL2, aes(RSA_x)) + geom_histogram()
p7 <- ggplot(hlfleL2, aes(RS_sweep)) + geom_histogram()
p8 <- ggplot(hlfleL2, aes(RSA_sweep)) + geom_histogram()
(p5 + p6) / (p7 + p8)
#> Warning: Removed 115 rows containing non-finite values (stat_bin).
#> Removed 2 rows containing missing values (geom_bar).
# Why do I have RSA values smaller than one? (either because sweep length is longer or gear is larger (GOV))
# Check if I can calculate the same RSA in sweep as that entered there.
# Ok, so the equation is correct. Which ID's have RSA < 1?
hlcodL2 %>%
filter(RSA_x < 1 & RSA_x > 0) %>%
dplyr::select(Year, Country, Ship, Gear, haul.id, Horizontal.opening..m., Fishing.line,
Swep.one.side..after.formula...meter, SweepLngt, Size.final..m, Swept.area, RSA_x) %>%
ggplot(., aes(RSA_x, fill = factor(SweepLngt))) + geom_histogram() + facet_wrap(~Gear, ncol = 1)
# Check if I have more than one unique RS or RSA value per haul, or if it's "either this or that"
# Filter positive in both columns
hlcodL2 %>% filter(RS_x > 0 & RS_sweep > 0) %>% ggplot(., aes(RS_x, RS_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
hlcodL2 %>% filter(RSA_x > 0 & RSA_sweep > 0) %>% ggplot(., aes(RSA_x, RSA_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
hlfleL2 %>% filter(RS_x > 0 & RS_sweep > 0) %>% ggplot(., aes(RS_x, RS_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
hlfleL2 %>% filter(RSA_x > 0 & RSA_sweep > 0) %>% ggplot(., aes(RSA_x, RSA_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
# Ok, there's on odd RS_x that is larger than 3. It didn't catch anything and speed is 0.8! Will remove
hlcodL2 <- hlcodL2 %>% filter(RS_x < 3)
hlfleL2 <- hlfleL2 %>% filter(RS_x < 3)
# Plot again
hlcodL2 %>% filter(RS_x > 0 & RS_sweep > 0) %>% ggplot(., aes(RS_x, RS_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
hlfleL2 %>% filter(RS_x > 0 & RS_sweep > 0) %>% ggplot(., aes(RS_x, RS_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
# They are largely the same when they overlap. When they differ, I will use RS_sweep
# Make a single RS and RSA column
# Cod
hlcodL3 <- hlcodL2 %>%
mutate(RS = -99,
RS = ifelse(RS_sweep > 0, RS_sweep, RS),
RS = ifelse(RS < 0 & RS_x > 0, RS_x, RS)) %>% # Note that there are no NA i RS_x. This replaces all non-RS_sweep values -0.3, so I can filter positive later
mutate(RSA = -99,
RSA = ifelse(RSA_sweep > 0, RSA_sweep, RSA),
RSA = ifelse(RSA < 0 & RSA_x > 0, RSA_x, RSA)) %>%
filter(RS > 0) %>%
filter(RSA > 0) %>%
mutate(RSRSA = RS*RSA)
# Plot
ggplot(hlcodL3, aes(RSRSA)) + geom_histogram()
hlfleL2 %>% filter(Country == "LAT") %>% distinct(Year) %>% arrange(Year)
#> # A tibble: 27 × 1
#> Year
#> <int>
#> 1 1991
#> 2 1993
#> 3 1995
#> 4 1996
#> 5 1997
#> 6 1999
#> 7 2000
#> 8 2001
#> 9 2002
#> 10 2003
#> # … with 17 more rows
# Flounder
hlfleL3 <- hlfleL2 %>%
mutate(RS = -999,
RS = ifelse(RS_sweep > 0, RS_sweep, RS),
RS = ifelse(RS < 0, RS_x, RS)) %>% # Note that there are no NA i RS_x. This replaces all non-RS_sweep values -0.3, so I can filter positive later
mutate(RSA = -999,
RSA = ifelse(RSA_sweep > 0, RSA_sweep, RSA),
RSA = ifelse(RSA < 0, RSA_x, RSA)) %>%
filter(RS > 0) %>%
filter(RSA > 0) %>%
mutate(RSRSA = RS*RSA)
# Test how many years of LAT data I miss out on because I can't standardize it.
# hlfleL2 %>%
# mutate(RS = -999,
# RS = ifelse(RS_sweep > 0, RS_sweep, RS),
# RS = ifelse(RS < 0, RS_x, RS)) %>% # Note that there are no NA i RS_x. This replaces all non-RS_sweep values -0.3, so I can filter positive later
# filter(RS > 0) %>%
# filter(Country == "LAT") %>%
# distinct(Year) %>%
# arrange(Year)
#
# hlfleL2 %>%
# mutate(RSA = -999,
# RSA = ifelse(RSA_sweep > 0, RSA_sweep, RSA),
# RSA = ifelse(RSA < 0, RSA_x, RSA)) %>%
# filter(RSA > 0) %>%
# filter(Country == "LAT") %>%
# distinct(Year) %>%
# arrange(Year)
# Plot
ggplot(hlcodL3, aes(RSRSA)) + geom_histogram()
# Standardize!
hlcodL3 <- hlcodL3 %>%
mutate(CPUEst_kg = CPUEun_kg*RS*RSA,
CPUEst = CPUEun*RS*RSA)
hlfleL3 <- hlfleL3 %>%
mutate(CPUEst_kg = CPUEun_kg*RS*RSA,
CPUEst = CPUEun*RS*RSA)
unique(is.na(hlcodL3$CPUEst_kg))
#> [1] FALSE
unique(is.na(hlcodL3$CPUEst))
#> [1] FALSE
min(hlcodL3$CPUEst_kg)
#> [1] 0
min(hlcodL3$CPUEst)
#> [1] 0
unique(is.na(hlfleL3$CPUEst_kg)) # Remove the few NA's here
#> [1] TRUE FALSE
hlfleL3 <- hlfleL3 %>% drop_na(CPUEst_kg)
unique(is.na(hlfleL3$CPUEst))
#> [1] FALSE
min(hlfleL3$CPUEst_kg)
#> [1] 0
min(hlfleL3$CPUEst)
#> [1] 0
# Now calculate total CPUE per haul in Orios unit, then create the new unit, i.e.:
# convert from kg of fish caught by trawling for 1 h a standard bottom swept area of
# 0.45km2 (using a TVL trawl with 75 m sweeps at the standard speed of three knots) to....
# kg of fish per km^2 by dividing with 0.45
hlcodhaul <- hlcodL3 %>%
group_by(haul.id) %>%
mutate(haul_cpue_kg = sum(CPUEst_kg),
haul_cpue = sum(CPUEst),
haul_cpue_kg_un = sum(CPUEun_kg),
haul_cpue_un = sum(CPUEun),
density = haul_cpue_kg/0.45,
density_ab = haul_cpue/0.45) %>%
ungroup() %>%
distinct(haul.id, .keep_all = TRUE)
# t <- hlcodhaul %>% filter(haul_cpue_un == 0)
# t <- hlcodhaul %>% filter(!Country == "SWE") %>% filter(haul_cpue_un > 0)
hlflehaul <- hlfleL3 %>%
group_by(haul.id) %>%
mutate(haul_cpue_kg = sum(CPUEst_kg),
haul_cpue = sum(CPUEst),
haul_cpue_kg_un = sum(CPUEun_kg),
haul_cpue_un = sum(CPUEun),
density = haul_cpue_kg/0.45,
density_ab = haul_cpue/0.45) %>%
ungroup() %>%
distinct(haul.id, .keep_all = TRUE)
# Rename things and select specific columns
dat <- hlcodhaul %>% rename("year" = "Year",
"lat" = "ShootLat",
"lon" = "ShootLong",
"quarter" = "Quarter",
"ices_rect" = "Rect",
"depth" = "Depth") %>%
dplyr::select(density, year, lat, lon, quarter, haul.id, IDx, ices_rect, sub_div, depth)
# Now add in the flounder data in the cod data based on haul ID
hlflehaul_select <- hlflehaul %>% mutate(density_fle = density) %>% dplyr::select(density_fle, haul.id)
dat <- left_join(dat, hlflehaul_select, by = "haul.id") %>% drop_na(density_fle)
dat %>% arrange(desc(density_fle)) %>% data.frame() %>% head(50)
#> density year lat lon quarter haul.id
#> 1 84.958409 2008 57.1850 20.6817 1 2008:1:LAT:BALL:TVL:1:4
#> 2 3107.363608 2008 56.2717 19.6533 1 2008:1:LAT:BALL:TVL:1:27
#> 3 45.808880 2007 56.9917 20.8033 1 2007:1:LAT:BALL:TVL:1:4
#> 4 74.676369 2010 57.3617 21.2350 4 2010:4:LAT:BALL:TVL:2:6
#> 5 295.261272 2012 56.5000 20.2150 1 2012:1:LAT:BALL:TVL:1:24
#> 6 102.464396 2011 56.9533 20.4150 1 2011:1:LAT:BALL:TVL:1:16
#> 7 242.575119 2008 57.2067 20.7283 1 2008:1:LAT:BALL:TVL:1:5
#> 8 13.872224 2010 57.2217 20.7167 1 2010:1:LAT:BALL:TVL:1:17
#> 9 51.843861 2007 55.7683 20.3333 1 2007:1:LAT:BALL:TVL:1:1
#> 10 95.664659 2007 57.2124 18.8491 4 2007:4:SWE:ARG:TVL:689:14
#> 11 6.508112 2009 57.8969 19.4288 1 2009:1:SWE:ARG:TVL:211:9
#> 12 6897.378835 2009 55.1083 19.7017 1 2009:1:RUS:ATL:TVL:57:4
#> 13 35.005848 2008 56.9350 20.3833 1 2008:1:LAT:BALL:TVL:1:1
#> 14 262.944923 2012 57.4600 21.2450 4 2012:4:LAT:BALL:TVL:2:6
#> 15 149.309819 2019 54.4500 19.3283 4 2019:4:POL:BAL:TVL:26256:4
#> 16 36.859878 2009 57.8994 19.4332 4 2009:4:SWE:ARG:TVL:723:11
#> 17 24.702776 2010 57.3550 20.5900 1 2010:1:LAT:BALL:TVL:1:15
#> 18 1153.634376 2014 55.4587 14.7253 1 2014:1:SWE:DAN2:TVL:48:26
#> 19 109.008774 2012 57.3683 21.2533 4 2012:4:LAT:BALL:TVL:2:7
#> 20 352.897387 2017 55.0233 16.3233 1 2017:1:POL:BAL:TVL:25066:28
#> 21 3499.177768 2011 55.3583 19.9267 1 2011:1:RUS:ATLD:TVL:56:40
#> 22 687.181689 2007 54.8692 15.4500 1 2007:1:POL:BAL:TVL:25159:31
#> 23 0.000000 2007 54.3692 19.2192 1 2007:1:POL:BAL:TVL:26001:35
#> 24 740.323341 2009 55.4950 20.0950 1 2009:1:RUS:ATL:TVL:57:10
#> 25 158.671747 1992 55.5833 15.8833 1 1992:1:GFR:SOL:CHP:25081:21
#> 26 31.348817 2001 57.1830 18.8793 1 2001:1:SWE:ARG:TVL:230:24
#> 27 3062.002553 2020 55.2367 17.3000 1 2020:1:POL:BAL:TVL:25463:9
#> 28 987.921853 2013 55.0750 19.6117 1 2013:1:RUS:ATLD:TVL:60:22
#> 29 53.784153 2007 56.5967 20.5100 1 2007:1:LAT:BALL:TVL:1:28
#> 30 4435.725757 2018 54.8317 19.6633 1 2018:1:RUS:ATL:TVL:67:2
#> 31 1838.109258 2001 56.4709 18.6149 4 2001:4:DEN:DAN2:TVL:93:38
#> 32 2176.245400 2020 55.2567 17.3700 1 2020:1:POL:BAL:TVL:25339:14
#> 33 62.375164 2020 54.4217 19.0267 1 2020:1:POL:BAL:TVL:26264:19
#> 34 181.453707 2008 57.1766 18.8081 4 2008:4:SWE:ARG:TVL:697:12
#> 35 883.537784 2019 55.4627 14.5290 4 2019:4:SWE:77SE:TVL:66:2
#> 36 1781.872489 2001 57.3537 19.1588 1 2001:1:SWE:ARG:TVL:236:29
#> 37 7210.671879 2014 55.4928 14.6408 1 2014:1:SWE:DAN2:TVL:51:27
#> 38 463.644551 2011 57.2433 21.0817 4 2011:4:LAT:BALL:TVL:2:11
#> 39 533.357067 1995 54.7667 15.6500 1 1995:1:GFR:SOL:H20:74:105
#> 40 274.848183 2001 55.0657 16.2425 1 2001:1:DEN:DAN2:TVL:81:39
#> 41 322.590582 2008 57.4733 21.1167 4 2008:4:LAT:BALL:TVL:2:3
#> 42 417.838920 2001 55.0489 16.2850 1 2001:1:DEN:DAN2:TVL:79:38
#> 43 511.328391 2016 54.7683 15.3100 1 2016:1:POL:BAL:TVL:25177:12
#> 44 24.325399 2007 57.2217 20.7467 1 2007:1:LAT:BALL:TVL:1:18
#> 45 22.230389 2011 57.0000 20.8117 1 2011:1:LAT:BALL:TVL:1:7
#> 46 1111.835196 2002 54.8461 15.3151 1 2002:1:DEN:DAN2:TVL:32:18
#> 47 1.255573 2008 57.0667 20.6367 1 2008:1:LAT:BALL:TVL:1:16
#> 48 9.415983 2013 57.7022 19.1408 4 2013:4:SWE:DAN2:TVL:33:17
#> 49 1642.707733 2007 57.2033 20.7167 4 2007:4:LAT:BALL:TVL:2:9
#> 50 1335.057913 2010 54.8850 18.8750 1 2010:1:POL:BAL:TVL:26087:33
#> IDx ices_rect sub_div depth density_fle
#> 1 2008.1.LAT.67BC.TVL.1.4 43H0 28 83 11169.594
#> 2 2008.1.LAT.67BC.TVL.1.27 41G9 26 73 9046.180
#> 3 2007.1.LAT.67BC.TVL.1.4 42H0 28 64 6636.965
#> 4 2010.4.LAT.67BC.TVL.2.6 43H1 28 64 6434.487
#> 5 2012.1.LAT.67BC.TVL.1.24 42H0 28 86 5782.487
#> 6 2011.1.LAT.67BC.TVL.1.16 42H0 28 86 5327.548
#> 7 2008.1.LAT.67BC.TVL.1.5 43H0 28 77 4178.697
#> 8 2010.1.LAT.67BC.TVL.1.17 43H0 28 81 3942.139
#> 9 2007.1.LAT.67BC.TVL.1.1 40H0 26 55 3200.816
#> 10 2007.4.SWE.77AR.TVL.689.14 43G8 28 43 3196.460
#> 11 2009.1.SWE.77AR.TVL.211.9 44G9 28 48 2807.984
#> 12 2009.1.RUS.RUNT.TVL.57.4 39G9 26 80 2712.294
#> 13 2008.1.LAT.67BC.TVL.1.1 42H0 28 80 2707.929
#> 14 2012.4.LAT.67BC.TVL.2.6 43H1 28 66 2680.423
#> 15 2019.4.POL.67BC.TVL.26256.4 37G9 26 62 2505.173
#> 16 2009.4.SWE.77AR.TVL.723.11 44G9 28 48 2481.592
#> 17 2010.1.LAT.67BC.TVL.1.15 43H0 28 87 2447.802
#> 18 2014.1.SWE.26D4.TVL.48.26 39G4 24 68 2442.102
#> 19 2012.4.LAT.67BC.TVL.2.7 43H1 28 55 2424.936
#> 20 2017.1.POL.67BC.TVL.25066.28 39G6 25 56 2416.610
#> 21 2011.1.RUS.RUJB.TVL.56.40 39G9 26 82 2415.328
#> 22 2007.1.POL.67BC.TVL.25159.31 38G5 25 77 2381.793
#> 23 2007.1.POL.67BC.TVL.26001.35 37G9 26 23 2357.677
#> 24 2009.1.RUS.RUNT.TVL.57.10 39H0 26 75 2341.579
#> 25 1992.1.GFR.06S1.CHP.25081.21 40G5 25 81 2286.580
#> 26 2001.1.SWE.77AR.TVL.230.24 43G8 28 53 2207.518
#> 27 2020.1.POL.67BC.TVL.25463.9 39G7 25 91 2206.927
#> 28 2013.1.RUS.RUJB.TVL.60.22 39G9 26 87 2188.610
#> 29 2007.1.LAT.67BC.TVL.1.28 42H0 28 64 2144.123
#> 30 2018.1.RUS.RUNT.TVL.67.2 38G9 26 60 2128.009
#> 31 2001.4.DEN.26D4.TVL.93.38 41G8 26 44 2098.998
#> 32 2020.1.POL.67BC.TVL.25339.14 39G7 25 88 2070.693
#> 33 2020.1.POL.67BC.TVL.26264.19 37G9 26 42 2063.225
#> 34 2008.4.SWE.77AR.TVL.697.12 43G8 28 40 2048.861
#> 35 2019.4.SWE.77SE.TVL.66.2 39G4 24 58 2038.147
#> 36 2001.1.SWE.77AR.TVL.236.29 43G9 28 81 2019.608
#> 37 2014.1.SWE.26D4.TVL.51.27 39G4 24 60 1999.449
#> 38 2011.4.LAT.67BC.TVL.2.11 43H1 28 61 1996.229
#> 39 1995.1.GFR.06S1.H20.74.105 38G5 25 78 1963.572
#> 40 2001.1.DEN.26D4.TVL.81.39 39G6 25 77 1909.892
#> 41 2008.4.LAT.67BC.TVL.2.3 43H1 28 71 1893.857
#> 42 2001.1.DEN.26D4.TVL.79.38 39G6 25 72 1870.911
#> 43 2016.1.POL.67BC.TVL.25177.12 38G5 25 68 1869.831
#> 44 2007.1.LAT.67BC.TVL.1.18 43H0 28 58 1829.415
#> 45 2011.1.LAT.67BC.TVL.1.7 43H0 28 57 1796.272
#> 46 2002.1.DEN.26D4.TVL.32.18 38G5 25 73 1765.521
#> 47 2008.1.LAT.67BC.TVL.1.16 43H0 28 88 1757.879
#> 48 2013.4.SWE.26D4.TVL.33.17 44G9 28 40 1696.887
#> 49 2007.4.LAT.67BC.TVL.2.9 43H0 28 75 1695.008
#> 50 2010.1.POL.67BC.TVL.26087.33 38G8 26 101 1692.796
# Filter unrealistically large densities
dat <- dat %>% filter(density_fle < 10000)
ggplot(dat, aes(density, density_fle)) + geom_point()
Compare cod data with Orio et al (2017)
# Read Orio data first for comparison:
test_cod <- hlcodhaul %>% filter(!Country == "SWE")
test_fle <- hlflehaul %>% filter(!Country == "SWE")
orio_cod <- read.csv("/Users/maxlindmark/Dropbox/Max work/R/gear-standardization-ale/datras_st_ale.csv")
orio_fle <- read.csv("/Users/maxlindmark/Dropbox/Max work/R/gear-standardization-ale/datras_fle_st_ale.csv")
orio_cod <- orio_cod %>%
group_by(IDX) %>%
mutate(haul_cpue_kg = sum(CPUEstBIOyear),
haul_cpue = sum(CPUEst),
haul_cpue_kg_un = sum(CPUEunBIOyear),
haul_cpue_un = sum(CPUEun)) %>%
ungroup() %>%
distinct(IDX, .keep_all = TRUE)
orio_fle <- orio_fle %>%
group_by(IDX) %>%
mutate(haul_cpue_kg = sum(CPUEstBIOyear),
haul_cpue = sum(CPUEst),
haul_cpue_kg_un = sum(CPUEunBIOyear),
haul_cpue_un = sum(CPUEun)) %>%
ungroup() %>%
distinct(IDX, .keep_all = TRUE)
# hlcodhaul %>% group_by(IDx) %>% mutate(n = n()) %>% ungroup() %>% distinct(n)
# Standardize data for easier plotting
test_cod_q1 <- test_cod %>%
dplyr::select(haul_cpue_kg, haul_cpue, haul_cpue_kg_un, haul_cpue_un, Year, Ship3, Country, Gear, Quarter) %>%
mutate(source = "Max",
species = "Cod") %>%
rename("Ship" = "Ship3") %>%
filter(Year > 1992 & Year < 2017 & Quarter == 1)
test_cod_q4 <- test_cod %>%
dplyr::select(haul_cpue_kg, haul_cpue, haul_cpue_kg_un, haul_cpue_un, Year, Ship3, Country, Gear, Quarter) %>%
mutate(source = "Max",
species = "Cod") %>%
rename("Ship" = "Ship3") %>%
filter(Year > 1992 & Year < 2017 & Quarter == 4)
test_fle_q1 <- test_fle %>%
dplyr::select(haul_cpue_kg, haul_cpue, haul_cpue_kg_un, haul_cpue_un, Year, Ship3, Country, Gear, Quarter) %>%
mutate(source = "Max",
species = "Fle") %>%
rename("Ship" = "Ship3") %>%
filter(Year > 1992 & Year < 2017 & Quarter == 1)
test_fle_q4 <- test_fle %>%
dplyr::select(haul_cpue_kg, haul_cpue, haul_cpue_kg_un, haul_cpue_un, Year, Ship3, Country, Gear, Quarter) %>%
mutate(source = "Max",
species = "Fle") %>%
rename("Ship" = "Ship3") %>%
filter(Year > 1992 & Year < 2017 & Quarter == 4)
orio_cod_q1 <- orio_cod %>%
dplyr::select(haul_cpue_kg, haul_cpue, haul_cpue_kg_un, haul_cpue_un, year, vessel, IDX) %>%
mutate(source = "Ale",
species = "Cod") %>%
rename("Year" = "year",
"Ship" = "vessel") %>%
mutate(IDX2 = IDX,
IDX3 = IDX) %>%
separate(IDX, sep = c(5:7), into = c("temp_year", "Quarter")) %>%
separate(temp_year, sep = c(4:5), into = c("Year", "scrap")) %>%
separate(IDX2, sep = 7, into = c("scrap2", "Country")) %>%
separate(Country, sep = 3, into = c("Country", "scrap3")) %>%
separate(IDX3, sep = 15, into = c("scrap4", "Gear")) %>%
separate(Gear, sep = 3, into = c("Gear", "scrap5")) %>%
dplyr::select(-scrap, -scrap2, -scrap3, -scrap4, -scrap5) %>%
filter(Quarter == 1) %>%
mutate(Year = as.integer(Year),
Quarter = as.integer(Quarter)) %>%
mutate(haul_cpue_kg = haul_cpue_kg/1000,
haul_cpue_kg_un = haul_cpue_kg_un/1000) %>%
filter(Year > 1992) %>%
filter(Country %in% unique(test_cod$Country))
orio_cod_q4 <- orio_cod %>%
dplyr::select(haul_cpue_kg, haul_cpue, haul_cpue_kg_un, haul_cpue_un, year, vessel, IDX) %>%
mutate(source = "Ale",
species = "Cod") %>%
rename("Year" = "year",
"Ship" = "vessel") %>%
mutate(IDX2 = IDX,
IDX3 = IDX) %>%
separate(IDX, sep = c(5:7), into = c("temp_year", "Quarter")) %>%
separate(temp_year, sep = c(4:5), into = c("Year", "scrap")) %>%
separate(IDX2, sep = 7, into = c("scrap2", "Country")) %>%
separate(Country, sep = 3, into = c("Country", "scrap3")) %>%
separate(IDX3, sep = 15, into = c("scrap4", "Gear")) %>%
separate(Gear, sep = 3, into = c("Gear", "scrap5")) %>%
dplyr::select(-scrap, -scrap2, -scrap3, -scrap4, -scrap5) %>%
filter(Quarter == 4) %>%
mutate(Year = as.integer(Year),
Quarter = as.integer(Quarter)) %>%
mutate(haul_cpue_kg = haul_cpue_kg/1000,
haul_cpue_kg_un = haul_cpue_kg_un/1000) %>%
filter(Year > 1992) %>%
filter(Country %in% unique(test_cod$Country))
orio_fle_q1 <- orio_fle %>%
dplyr::select(haul_cpue_kg, haul_cpue, haul_cpue_kg_un, haul_cpue_un, year, vessel, IDX) %>%
mutate(source = "Ale",
species = "Fle") %>%
rename("Year" = "year",
"Ship" = "vessel") %>%
mutate(IDX2 = IDX,
IDX3 = IDX) %>%
separate(IDX, sep = c(5:7), into = c("temp_year", "Quarter")) %>%
separate(temp_year, sep = c(4:5), into = c("Year", "scrap")) %>%
separate(IDX2, sep = 7, into = c("scrap2", "Country")) %>%
separate(Country, sep = 3, into = c("Country", "scrap3")) %>%
separate(IDX3, sep = 15, into = c("scrap4", "Gear")) %>%
separate(Gear, sep = 3, into = c("Gear", "scrap5")) %>%
dplyr::select(-scrap, -scrap2, -scrap3, -scrap4, -scrap5) %>%
filter(Quarter == 1) %>%
mutate(Year = as.integer(Year),
Quarter = as.integer(Quarter)) %>%
mutate(haul_cpue_kg = haul_cpue_kg/1000,
haul_cpue_kg_un = haul_cpue_kg_un/1000) %>%
filter(Year > 1992) %>%
filter(Country %in% unique(test_cod$Country))
orio_fle_q4 <- orio_fle %>%
dplyr::select(haul_cpue_kg, haul_cpue, haul_cpue_kg_un, haul_cpue_un, year, vessel, IDX) %>%
mutate(source = "Ale",
species = "Fle") %>%
rename("Year" = "year",
"Ship" = "vessel") %>%
mutate(IDX2 = IDX,
IDX3 = IDX) %>%
separate(IDX, sep = c(5:7), into = c("temp_year", "Quarter")) %>%
separate(temp_year, sep = c(4:5), into = c("Year", "scrap")) %>%
separate(IDX2, sep = 7, into = c("scrap2", "Country")) %>%
separate(Country, sep = 3, into = c("Country", "scrap3")) %>%
separate(IDX3, sep = 15, into = c("scrap4", "Gear")) %>%
separate(Gear, sep = 3, into = c("Gear", "scrap5")) %>%
dplyr::select(-scrap, -scrap2, -scrap3, -scrap4, -scrap5) %>%
filter(Quarter == 4) %>%
mutate(Year = as.integer(Year),
Quarter = as.integer(Quarter)) %>%
mutate(haul_cpue_kg = haul_cpue_kg/1000,
haul_cpue_kg_un = haul_cpue_kg_un/1000) %>%
filter(Year > 1992) %>%
filter(Country %in% unique(test_cod$Country))
sort(unique(test_cod_q1$Country))
#> [1] "DEN" "GFR" "LAT" "LTU" "POL" "RUS"
sort(unique(orio_cod_q1$Country))
#> [1] "DEN" "GFR" "LAT" "LTU" "POL" "RUS"
# Check proportions of zero
t <- test_cod_q1 %>% filter(haul_cpue_kg > 0)
t <- orio_cod_q1 %>% filter(haul_cpue_kg > 0)
t <- test_cod_q4 %>% filter(haul_cpue_kg > 0)
t <- orio_cod_q4 %>% filter(haul_cpue_kg > 0)
t <- test_fle_q1 %>% filter(haul_cpue_kg > 0)
t <- orio_fle_q1 %>% filter(haul_cpue_kg > 0)
t <- test_fle_q4 %>% filter(haul_cpue_kg > 0)
t <- orio_fle_q4 %>% filter(haul_cpue_kg > 0)
test_full <- bind_rows(orio_fle_q1, orio_fle_q4, orio_cod_q1, orio_cod_q4,
test_cod_q1, test_cod_q4, test_fle_q1, test_fle_q4)
# Check the non-standardized data for cod
# Raw abundance cpue
test_full %>%
filter(species == "Cod") %>%
ggplot(., aes(factor(Year), haul_cpue_un, color = source)) +
geom_point(size = 1, position = position_dodge(width = 0.5)) +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(~Quarter)
test_full %>%
filter(species == "Cod" & Quarter == 1) %>%
ggplot(., aes(haul_cpue_un, fill = source)) +
geom_histogram(position = position_dodge()) +
facet_wrap(~Year, scale = "free") +
theme(axis.text.x = element_text(angle = 90))
test_full %>%
filter(species == "Cod" & Quarter == 4) %>%
ggplot(., aes(haul_cpue_un, fill = source)) +
geom_histogram(position = position_dodge()) +
facet_wrap(~Year, scale = "free") +
theme(axis.text.x = element_text(angle = 90))
# Mean abundance cpue
test_full %>%
filter(species == "Cod") %>%
group_by(Year, Quarter, source) %>%
summarise(mean_cpue = mean(haul_cpue_un),
sd_cpue = sd(haul_cpue_un)) %>%
ggplot(., aes(Year, mean_cpue, linetype = source, color = source)) +
geom_line(size = 1.1) +
geom_point(size = 3) +
facet_wrap(~Quarter, ncol = 1)
# Mean abundance cpue by country
test_full %>%
filter(species == "Cod") %>%
group_by(Year, source, Quarter, Country) %>%
summarise(mean_cpue = mean(haul_cpue_un),
sd_cpue = sd(haul_cpue_un)) %>%
ggplot(., aes(Year, mean_cpue, linetype = source, color = source)) +
geom_line(size = 1.1) +
facet_grid(Quarter ~ Country)
# Mean abundance cpue for non-zero cathes
test_full %>%
filter(species == "Cod") %>%
filter(haul_cpue_un > 0) %>%
group_by(Year, Quarter, source) %>%
summarise(mean_cpue = mean(haul_cpue_un),
sd_cpue = sd(haul_cpue_un)) %>%
ggplot(., aes(Year, mean_cpue, linetype = source, color = source)) +
geom_line(size = 1.1) +
geom_point(size = 3) +
facet_wrap(~ Quarter, ncol = 1)
# Now plot corrected biomass cpue
test_full %>%
filter(species == "Cod") %>%
ggplot(., aes(factor(Year), haul_cpue_kg, color = source)) +
geom_point(size = 1, position = position_dodge(width = 0.5)) +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(~ Quarter, ncol = 1)
# Mean corrected biomass cpue
test_full %>%
filter(species == "Cod") %>%
group_by(Year, Quarter, source) %>%
summarise(mean_cpue = mean(haul_cpue_kg),
sd_cpue = sd(haul_cpue_kg)) %>%
ggplot(., aes(Year, mean_cpue, linetype = source, color = source)) +
geom_line(size = 1.1) +
geom_point(size = 3) +
facet_wrap(~ Quarter, ncol = 1)
# Now check in on flounder
# Raw abundance cpue
test_full %>%
filter(species == "Fle") %>%
ggplot(., aes(factor(Year), haul_cpue_un, color = source)) +
geom_point(size = 1, position = position_dodge(width = 0.5)) +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(~ Quarter, ncol = 1)
test_full %>%
filter(species == "Fle" & Quarter == 1) %>%
ggplot(., aes(haul_cpue_un, fill = source)) +
geom_histogram(position = position_dodge()) +
facet_wrap(~Year, scale = "free") +
theme(axis.text.x = element_text(angle = 90))
test_full %>%
filter(species == "Fle" & Quarter == 4) %>%
ggplot(., aes(haul_cpue_un, fill = source)) +
geom_histogram(position = position_dodge()) +
facet_wrap(~Year, scale = "free") +
theme(axis.text.x = element_text(angle = 90))
# Mean abundance cpue
test_full %>%
filter(species == "Fle") %>%
group_by(Year, Quarter, source) %>%
summarise(mean_cpue = mean(haul_cpue_un),
sd_cpue = sd(haul_cpue_un)) %>%
ggplot(., aes(Year, mean_cpue, linetype = source, color = source)) +
geom_line(size = 1.1) +
geom_point(size = 3) +
facet_wrap(~ Quarter, ncol = 1)
# Ok, here we can see that Ale predicts an increase earlier, which we also saw
# on the plot of raw catches as points
# Mean abundance cpue by country to see how this can arise
test_full %>%
filter(species == "Fle") %>%
group_by(Year, Quarter, source, Country) %>%
summarise(mean_cpue = mean(haul_cpue_un),
sd_cpue = sd(haul_cpue_un)) %>%
ggplot(., aes(Year, mean_cpue, linetype = source, color = source)) +
geom_line(size = 1.1) +
facet_grid(Quarter ~ Country)
# Ok, after checking, the reason I don't have the older LAT data is because I don't
# have RS or RSA-values for those hauls, even though the catch data are in datras...
# Should probably check with Ale how he corrected those!
# Now plot corrected biomass cpue
test_full %>%
filter(species == "Fle") %>%
ggplot(., aes(factor(Year), haul_cpue_kg, color = source)) +
geom_point(size = 1, position = position_dodge(width = 0.5)) +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(~ Quarter, ncol = 1)
# Mean corrected biomass cpue
test_full %>%
filter(species == "Fle") %>%
group_by(Year, Quarter, source) %>%
summarise(mean_cpue = mean(haul_cpue_kg),
sd_cpue = sd(haul_cpue_kg)) %>%
ggplot(., aes(Year, mean_cpue, linetype = source, color = source)) +
geom_line(size = 1.1) +
geom_point(size = 3) +
facet_wrap(~ Quarter, ncol = 1)
# Then check length-weight relationships againAdd in the environmental variables
Depth
# Read the tifs
west <- raster("data/depth_geo_tif/D5_2018_rgb-1.tif")
#plot(west)
east <- raster("data/depth_geo_tif/D6_2018_rgb-1.tif")
# plot(east)
dep_rast <- raster::merge(west, east)
dat$depth_r <- extract(dep_rast, dat %>% dplyr::select(lon, lat), method = "bilinear")
# Convert to depth (instead of elevation)
ggplot(dat, aes(depth)) + geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> Warning: Removed 1 rows containing non-finite values (stat_bin).
dat$depth_r <- (dat$depth_r - max(drop_na(dat)$depth_r)) *-1
#> drop_na: removed one row (<1%), 8,938 rows remaining
ggplot(dat, aes(depth_r)) + geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(dat, aes(depth)) + geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> Warning: Removed 1 rows containing non-finite values (stat_bin).
ggplot(dat, aes(depth_r, depth)) + geom_point() + geom_abline(color = "red")
#> Warning: Removed 1 rows containing missing values (geom_point).
ggplot(dat, aes(depth_r, depth)) +
geom_point() +
geom_abline(color = "red") +
coord_cartesian(xlim = c(0, 20), ylim = c(0, 75)) +
geom_vline(xintercept = 10, color = "blue") +
geom_hline(yintercept = 10, color = "blue")
#> Warning: Removed 1 rows containing missing values (geom_point).
ggplot(dat, aes(depth_r, depth)) +
geom_point() +
geom_abline(color = "red") +
coord_cartesian(xlim = c(90, 130), ylim = c(75, 125)) +
geom_vline(xintercept = 110, color = "blue") +
geom_hline(yintercept = 110, color = "blue")
#> Warning: Removed 1 rows containing missing values (geom_point).
# So, roughly 3 meter is 10, and 120 is 110
summary(dat$depth)
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 10.00 40.00 54.00 55.12 71.00 190.00 1
quantile(drop_na(dat)$depth, prob = c(0.01, 0.99))
#> drop_na: removed one row (<1%), 8,938 rows remaining
#> 1% 99%
#> 16 108
summary(dat$depth_r)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00 25.31 43.89 46.81 66.00 165.47
dat <- dat %>%
dplyr::select(-depth) %>%
rename(depth = depth_r)
#> rename: renamed one variable (depth)Oxygen
# Rename fishdata to fishdat so that I don't overwrite anything. Not all years in the BITS
# have environmental data, and I don't want to loose important trawl information
fishdat <- dat
# Downloaded from here: https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_BIO_003_012
# Extract raster points: https://gisday.wordpress.com/2014/03/24/extract-raster-values-from-points-using-r/comment-page-1/
# https://rpubs.com/boyerag/297592
# https://pjbartlein.github.io/REarthSysSci/netCDF.html#get-a-variable
# Open the netCDF file
ncin <- nc_open("data/NEMO_Nordic_SCOBI/dataset-reanalysis-scobi-monthlymeans_1610091357600.nc")
print(ncin)
#> File data/NEMO_Nordic_SCOBI/dataset-reanalysis-scobi-monthlymeans_1610091357600.nc (NC_FORMAT_CLASSIC):
#>
#> 1 variables (excluding dimension variables):
#> float o2b[longitude,latitude,time]
#> long_name: Sea_floor_Dissolved_Oxygen_Concentration
#> missing_value: NaN
#> standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
#> units: mmol m-3
#> _FillValue: NaN
#> _ChunkSizes: 1
#> _ChunkSizes: 523
#> _ChunkSizes: 383
#>
#> 3 dimensions:
#> time Size:324
#> axis: T
#> long_name: Validity time
#> standard_name: time
#> units: days since 1950-01-01 00:00:00
#> calendar: gregorian
#> _ChunkSizes: 512
#> _CoordinateAxisType: Time
#> valid_min: 15721.5
#> valid_max: 25551.5
#> latitude Size:166
#> axis: Y
#> standard_name: latitude
#> long_name: latitude
#> units: degrees_north
#> _CoordinateAxisType: Lat
#> valid_min: 52.991626739502
#> valid_max: 58.4915390014648
#> longitude Size:253
#> standard_name: longitude
#> long_name: longitude
#> units: degrees_east
#> axis: X
#> _CoordinateAxisType: Lon
#> valid_min: 9.01375484466553
#> valid_max: 23.013614654541
#>
#> 24 global attributes:
#> references: http://www.smhi.se
#> institution: Swedish Meterological and Hydrological Institute
#> history: See source and creation_date attributees
#> Conventions: CF-1.5
#> contact: servicedesk_cmems@mercator-ocean.eu
#> comment: Provided by SMHI as a Copernicus Marine Environment Monitoring Service production unit
#> bullentin_type: reanalysis
#> cmems_product_id: BALTICSEA_REANALYSIS_BIO_003_012
#> title: CMEMS V4 Reanalysis: SCOBI model 3D fields (monthly means)
#> FROM_ORIGINAL_FILE__easternmost_longitude: 30.2357654571533
#> FROM_ORIGINAL_FILE__northernmost_latitude: 65.8914184570312
#> FROM_ORIGINAL_FILE__westernmost_longitude: 9.01375484466553
#> FROM_ORIGINAL_FILE__southernmost_latitude: 48.49169921875
#> shallowest_depth: 1.50136542320251
#> deepest_depth: 711.059204101562
#> source: SMHI reanalysis run NORDIC-NS2_1d_20191201_20191202
#> file_quality_index: 1
#> creation_date: 2020-11-16 UTC
#> bullentin_date: 20191201
#> start_date: 2019-12-01 UTC
#> stop_date: 2019-12-01 UTC
#> start_time: 00:00 UTC
#> stop_time: 00:00 UTC
#> _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
# Get longitude and latitude
lon <- ncvar_get(ncin,"longitude")
nlon <- dim(lon)
head(lon)
#> [1] 9.013755 9.069310 9.124865 9.180420 9.235975 9.291530
lat <- ncvar_get(ncin,"latitude")
nlat <- dim(lat)
head(lat)
#> [1] 52.99163 53.02496 53.05829 53.09163 53.12496 53.15829
# Get time
time <- ncvar_get(ncin,"time")
time
#> [1] 15721.5 15751.0 15780.5 15811.0 15841.5 15872.0 15902.5 15933.5 15964.0
#> [10] 15994.5 16025.0 16055.5 16086.5 16116.0 16145.5 16176.0 16206.5 16237.0
#> [19] 16267.5 16298.5 16329.0 16359.5 16390.0 16420.5 16451.5 16481.0 16510.5
#> [28] 16541.0 16571.5 16602.0 16632.5 16663.5 16694.0 16724.5 16755.0 16785.5
#> [37] 16816.5 16846.5 16876.5 16907.0 16937.5 16968.0 16998.5 17029.5 17060.0
#> [46] 17090.5 17121.0 17151.5 17182.5 17212.0 17241.5 17272.0 17302.5 17333.0
#> [55] 17363.5 17394.5 17425.0 17455.5 17486.0 17516.5 17547.5 17577.0 17606.5
#> [64] 17637.0 17667.5 17698.0 17728.5 17759.5 17790.0 17820.5 17851.0 17881.5
#> [73] 17912.5 17942.0 17971.5 18002.0 18032.5 18063.0 18093.5 18124.5 18155.0
#> [82] 18185.5 18216.0 18246.5 18277.5 18307.5 18337.5 18368.0 18398.5 18429.0
#> [91] 18459.5 18490.5 18521.0 18551.5 18582.0 18612.5 18643.5 18673.0 18702.5
#> [100] 18733.0 18763.5 18794.0 18824.5 18855.5 18886.0 18916.5 18947.0 18977.5
#> [109] 19008.5 19038.0 19067.5 19098.0 19128.5 19159.0 19189.5 19220.5 19251.0
#> [118] 19281.5 19312.0 19342.5 19373.5 19403.0 19432.5 19463.0 19493.5 19524.0
#> [127] 19554.5 19585.5 19616.0 19646.5 19677.0 19707.5 19738.5 19768.5 19798.5
#> [136] 19829.0 19859.5 19890.0 19920.5 19951.5 19982.0 20012.5 20043.0 20073.5
#> [145] 20104.5 20134.0 20163.5 20194.0 20224.5 20255.0 20285.5 20316.5 20347.0
#> [154] 20377.5 20408.0 20438.5 20469.5 20499.0 20528.5 20559.0 20589.5 20620.0
#> [163] 20650.5 20681.5 20712.0 20742.5 20773.0 20803.5 20834.5 20864.0 20893.5
#> [172] 20924.0 20954.5 20985.0 21015.5 21046.5 21077.0 21107.5 21138.0 21168.5
#> [181] 21199.5 21229.5 21259.5 21290.0 21320.5 21351.0 21381.5 21412.5 21443.0
#> [190] 21473.5 21504.0 21534.5 21565.5 21595.0 21624.5 21655.0 21685.5 21716.0
#> [199] 21746.5 21777.5 21808.0 21838.5 21869.0 21899.5 21930.5 21960.0 21989.5
#> [208] 22020.0 22050.5 22081.0 22111.5 22142.5 22173.0 22203.5 22234.0 22264.5
#> [217] 22295.5 22325.0 22354.5 22385.0 22415.5 22446.0 22476.5 22507.5 22538.0
#> [226] 22568.5 22599.0 22629.5 22660.5 22690.5 22720.5 22751.0 22781.5 22812.0
#> [235] 22842.5 22873.5 22904.0 22934.5 22965.0 22995.5 23026.5 23056.0 23085.5
#> [244] 23116.0 23146.5 23177.0 23207.5 23238.5 23269.0 23299.5 23330.0 23360.5
#> [253] 23391.5 23421.0 23450.5 23481.0 23511.5 23542.0 23572.5 23603.5 23634.0
#> [262] 23664.5 23695.0 23725.5 23756.5 23786.0 23815.5 23846.0 23876.5 23907.0
#> [271] 23937.5 23968.5 23999.0 24029.5 24060.0 24090.5 24121.5 24151.5 24181.5
#> [280] 24212.0 24242.5 24273.0 24303.5 24334.5 24365.0 24395.5 24426.0 24456.5
#> [289] 24487.5 24517.0 24546.5 24577.0 24607.5 24638.0 24668.5 24699.5 24730.0
#> [298] 24760.5 24791.0 24821.5 24852.5 24882.0 24911.5 24942.0 24972.5 25003.0
#> [307] 25033.5 25064.5 25095.0 25125.5 25156.0 25186.5 25217.5 25247.0 25276.5
#> [316] 25307.0 25337.5 25368.0 25398.5 25429.5 25460.0 25490.5 25521.0 25551.5
tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt
#> [1] 324
tunits
#> $hasatt
#> [1] TRUE
#>
#> $value
#> [1] "days since 1950-01-01 00:00:00"
# Get oxygen
dname <- "o2b"
oxy_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(oxy_array)
#> [1] 253 166 324
# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")
# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])
# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))
# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)
# Replace netCDF fill values with NA's
oxy_array[oxy_array == fillvalue$value] <- NA
# Next, we need to work with the months that correspond to the quarters that we use.
# loop through each time step, and if it is a good month save it as a raster.
# First get the index of months that correspond to Q4
months
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1
#> [26] 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2
#> [51] 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3
#> [76] 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4
#> [101] 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5
#> [126] 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6
#> [151] 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7
#> [176] 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8
#> [201] 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9
#> [226] 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10
#> [251] 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11
#> [276] 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
#> [301] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
index_keep_q1 <- which(months < 4)
index_keep_q4 <- which(months > 9)
oxy_q1 <- oxy_array[, , index_keep_q1]
oxy_q4 <- oxy_array[, , index_keep_q4]
months_keep_q1 <- months[index_keep_q1]
months_keep_q4 <- months[index_keep_q4]
years_keep_q1 <- years[index_keep_q1]
years_keep_q4 <- years[index_keep_q4]
# Now we have an array with only Q4 data...
# We need to now calculate the average within a year.
# Get a sequence that takes every third value between 1: number of months (length)
loop_seq_q1 <- seq(1, dim(oxy_q1)[3], by = 3)
loop_seq_q4 <- seq(1, dim(oxy_q4)[3], by = 3)
# Create objects that will hold data
dlist_q1 <- list()
dlist_q4 <- list()
oxy_1 <- c()
oxy_2 <- c()
oxy_3 <- c()
oxy_ave_q1 <- c()
oxy_10 <- c()
oxy_11 <- c()
oxy_12 <- c()
oxy_ave_q4 <- c()
# Now average by quarter. The vector loop_seq_q1 is 1, 4, 7 etc. So first i is 1, 2, 3,
# which is the index we want.
for(i in loop_seq_q1) { # We can use q1 as looping index, doesn't matter!
oxy_1 <- oxy_q1[, , (i)]
oxy_2 <- oxy_q1[, , (i + 1)]
oxy_3 <- oxy_q1[, , (i + 2)]
oxy_10 <- oxy_q4[, , (i)]
oxy_11 <- oxy_q4[, , (i + 1)]
oxy_12 <- oxy_q4[, , (i + 2)]
oxy_ave_q1 <- (oxy_1 + oxy_2 + oxy_3) / 3
oxy_ave_q4 <- (oxy_10 + oxy_11 + oxy_12) / 3
list_pos_q1 <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
list_pos_q4 <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
dlist_q1[[list_pos_q1]] <- oxy_ave_q1
dlist_q4[[list_pos_q4]] <- oxy_ave_q4
}
# Now name the lists with the year:
names(dlist_q1) <- unique(years_keep_q1)
names(dlist_q4) <- unique(years_keep_q4)
# Now I need to make a loop where I extract the raster value for each year...
# The cpue data is called dat so far in this script
# Filter years in the cpue data frame to only have the years I have oxygen for
# Note: I will append the most recent data in the end, event though we don't have
# environmental data for it
d_sub_oxy_q1 <- dat %>% filter(quarter == 1) %>% filter(year %in% names(dlist_q1)) %>% droplevels()
#> filter: removed 3,586 rows (40%), 5,353 rows remaining
#> filter: removed 209 rows (4%), 5,144 rows remaining
d_sub_oxy_q4 <- dat %>% filter(quarter == 4) %>% filter(year %in% names(dlist_q4)) %>% droplevels()
#> filter: removed 5,353 rows (60%), 3,586 rows remaining
#> filter: removed 133 rows (4%), 3,453 rows remaining
# Create data holding object
data_list_q1 <- list()
data_list_q4 <- list()
# ... And for the oxygen raster
raster_list_q1 <- list()
raster_list_q4 <- list()
# Create factor year for indexing the list in the loop
d_sub_oxy_q1$year_f <- as.factor(d_sub_oxy_q1$year)
d_sub_oxy_q4$year_f <- as.factor(d_sub_oxy_q4$year)
# Loop through each year and extract raster values for the cpue data points
for(i in unique(d_sub_oxy_q1$year_f)) { # We can use q1 as looping index, doesn't matter!
# Set plot limits
ymin = 54; ymax = 58; xmin = 12; xmax = 22
# Subset a year
oxy_slice_q1 <- dlist_q1[[i]]
oxy_slice_q4 <- dlist_q4[[i]]
# Create raster for that year (i)
r_q1 <- raster(t(oxy_slice_q1), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
r_q4 <- raster(t(oxy_slice_q4), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
# Flip...
r_q1 <- flip(r_q1, direction = 'y')
r_q4 <- flip(r_q4, direction = 'y')
plot(r_q1, main = i)
plot(r_q4, main = i)
# Filter the same year (i) in the cpue data and select only coordinates
d_slice_q1 <- d_sub_oxy_q1 %>% filter(year_f == i) %>% dplyr::select(lon, lat)
d_slice_q4 <- d_sub_oxy_q4 %>% filter(year_f == i) %>% dplyr::select(lon, lat)
# Make into a SpatialPoints object
data_sp_q1 <- SpatialPoints(d_slice_q1)
data_sp_q4 <- SpatialPoints(d_slice_q4)
# Extract raster value (oxygen)
rasValue_q1 <- raster::extract(r_q1, data_sp_q1, method = "bilinear")
rasValue_q4 <- raster::extract(r_q4, data_sp_q4, method = "bilinear")
# Now we want to plot the results of the raster extractions by plotting the cpue
# data points over a raster and saving it for each year.
# Make the SpatialPoints object into a raster again (for pl)
df_q1 <- as.data.frame(data_sp_q1)
df_q4 <- as.data.frame(data_sp_q4)
# Add in the raster value in the df holding the coordinates for the cpue data
d_slice_q1$oxy <- rasValue_q1
d_slice_q4$oxy <- rasValue_q4
# Add in which year
d_slice_q1$year <- i
d_slice_q4$year <- i
# Create a index for the data last where we store all years (because our loop index
# i is not continuous, we can't use it directly)
index_q1 <- as.numeric(as.character(d_slice_q1$year))[1] - 1992
index_q4 <- as.numeric(as.character(d_slice_q4$year))[1] - 1992
# Add each years' data in the list
data_list_q1[[index_q1]] <- d_slice_q1
data_list_q4[[index_q4]] <- d_slice_q4
# Save to check each year is ok! First convert the raster to points for plotting
# (so that we can use ggplot)
map_q1 <- rasterToPoints(r_q1)
map_q4 <- rasterToPoints(r_q4)
# Make the points a dataframe for ggplot
df_rast_q1 <- data.frame(map_q1)
df_rast_q4 <- data.frame(map_q4)
# Rename y-variable and add year
df_rast_q1 <- df_rast_q1 %>% rename("oxy" = "layer") %>% mutate(year = i)
df_rast_q4 <- df_rast_q4 %>% rename("oxy" = "layer") %>% mutate(year = i)
# Add each years' raster data frame in the list
raster_list_q1[[index_q1]] <- df_rast_q1
raster_list_q4[[index_q4]] <- df_rast_q4
# Make appropriate column headings
colnames(df_rast_q1) <- c("Longitude", "Latitude", "oxy")
colnames(df_rast_q4) <- c("Longitude", "Latitude", "oxy")
# Make a map for q1
ggplot(data = df_rast_q1, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = oxy)) +
geom_point(data = d_slice_q1, aes(x = lon, y = lat, fill = oxy),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(-200, 400)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(-200, 400))
ggsave(paste("figures/supp/density/density_oxygen_rasters/", i,"q1.png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
# Make a map for q4
ggplot(data = df_rast_q4, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = oxy)) +
geom_point(data = d_slice_q4, aes(x = lon, y = lat, fill = oxy),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(-200, 400)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(-200, 400))
ggsave(paste("figures/supp/density/density_oxygen_rasters/", i,"q4.png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
}
#> filter: removed 5,043 rows (98%), 101 rows remaining
#> filter: removed 3,392 rows (98%), 61 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,001 rows (97%), 143 rows remaining
#> filter: removed 3,391 rows (98%), 62 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,990 rows (97%), 154 rows remaining
#> filter: removed 3,400 rows (98%), 53 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,009 rows (97%), 135 rows remaining
#> filter: removed 3,392 rows (98%), 61 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,000 rows (97%), 144 rows remaining
#> filter: removed 3,378 rows (98%), 75 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,969 rows (97%), 175 rows remaining
#> filter: removed 3,385 rows (98%), 68 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,973 rows (97%), 171 rows remaining
#> filter: removed 3,359 rows (97%), 94 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,034 rows (98%), 110 rows remaining
#> filter: removed 3,364 rows (97%), 89 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,937 rows (96%), 207 rows remaining
#> filter: removed 3,338 rows (97%), 115 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,002 rows (97%), 142 rows remaining
#> filter: removed 3,336 rows (97%), 117 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,008 rows (97%), 136 rows remaining
#> filter: removed 3,350 rows (97%), 103 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,968 rows (97%), 176 rows remaining
#> filter: removed 3,368 rows (98%), 85 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,959 rows (96%), 185 rows remaining
#> filter: removed 3,332 rows (96%), 121 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,956 rows (96%), 188 rows remaining
#> filter: removed 3,303 rows (96%), 150 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,909 rows (95%), 235 rows remaining
#> filter: removed 3,291 rows (95%), 162 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,923 rows (96%), 221 rows remaining
#> filter: removed 3,277 rows (95%), 176 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,894 rows (95%), 250 rows remaining
#> filter: removed 3,297 rows (95%), 156 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,904 rows (95%), 240 rows remaining
#> filter: removed 3,291 rows (95%), 162 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,901 rows (95%), 243 rows remaining
#> filter: removed 3,273 rows (95%), 180 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,920 rows (96%), 224 rows remaining
#> filter: removed 3,320 rows (96%), 133 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,876 rows (95%), 268 rows remaining
#> filter: removed 3,305 rows (96%), 148 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,936 rows (96%), 208 rows remaining
#> filter: removed 3,277 rows (95%), 176 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,907 rows (95%), 237 rows remaining
#> filter: removed 3,287 rows (95%), 166 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,912 rows (95%), 232 rows remaining
#> filter: removed 3,240 rows (94%), 213 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,899 rows (95%), 245 rows remaining
#> filter: removed 3,228 rows (93%), 225 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 4,888 rows (95%), 256 rows remaining
#> filter: removed 3,243 rows (94%), 210 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,026 rows (98%), 118 rows remaining
#> filter: removed 3,361 rows (97%), 92 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
# Now create a data frame from the list of all annual values
big_dat_oxy_q1 <- dplyr::bind_rows(data_list_q1)
big_dat_oxy_q4 <- dplyr::bind_rows(data_list_q4)
big_dat_oxy <- bind_rows(mutate(big_dat_oxy_q1, quarter = 1),
mutate(big_dat_oxy_q4, quarter = 4))
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
big_raster_dat_oxy_q1 <- dplyr::bind_rows(raster_list_q1)
big_raster_dat_oxy_q4 <- dplyr::bind_rows(raster_list_q4)
big_raster_dat_oxy <- bind_rows(mutate(big_raster_dat_oxy_q1, quarter = 1),
mutate(big_raster_dat_oxy_q4, quarter = 4))
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
# Plot data, looks like there's big inter-annual variation but a negative trend over time
big_raster_dat_oxy %>%
group_by(quarter, year) %>%
drop_na(oxy) %>%
summarise(mean_oxy = mean(oxy)) %>%
mutate(year_num = as.numeric(year)) %>%
ggplot(., aes(year_num, mean_oxy)) +
geom_point(size = 2) +
stat_smooth(method = "lm") +
facet_wrap(~ quarter)
#> group_by: 2 grouping variables (quarter, year)
#> drop_na (grouped): no rows removed
#> summarise: now 54 rows and 3 columns, one group variable remaining (quarter)
#> mutate (grouped): new variable 'year_num' (double) with 27 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'
big_raster_dat_oxy %>%
group_by(quarter, year) %>%
drop_na(oxy) %>%
mutate(dead = ifelse(oxy < 0, "Y", "N")) %>%
filter(dead == "Y") %>%
mutate(n = n(),
year_num = as.numeric(year)) %>%
ggplot(., aes(year_num, n)) +
geom_point(size = 2) +
stat_smooth(method = "lm") +
facet_wrap(~ quarter)
#> group_by: 2 grouping variables (quarter, year)
#> drop_na (grouped): no rows removed
#> mutate (grouped): new variable 'dead' (character) with 2 unique values and 0% NA
#> filter (grouped): removed 877,004 rows (94%), 60,814 rows remaining
#> mutate (grouped): new variable 'n' (integer) with 54 unique values and 0% NA
#> new variable 'year_num' (double) with 27 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'
# Now add in the new oxygen column in the original data:
str(d_sub_oxy_q1)
#> tibble [5,144 × 12] (S3: tbl_df/tbl/data.frame)
#> $ density : num [1:5144] 158.15 10.33 5.26 19.55 812.16 ...
#> $ year : int [1:5144] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:5144] 54.7 54.5 54.5 54.5 54.7 ...
#> $ lon : num [1:5144] 13 14.3 14.2 14 13.1 ...
#> $ quarter : int [1:5144] 1 1 1 1 1 1 1 1 1 1 ...
#> $ haul.id : chr [1:5144] "1993:1:GFR:SOL:H20:21:1" "1993:1:GFR:SOL:H20:22:32" "1993:1:GFR:SOL:H20:23:31" "1993:1:GFR:SOL:H20:24:30" ...
#> $ IDx : chr [1:5144] "1993.1.GFR.06S1.H20.21.1" "1993.1.GFR.06S1.H20.22.32" "1993.1.GFR.06S1.H20.23.31" "1993.1.GFR.06S1.H20.24.30" ...
#> $ ices_rect : chr [1:5144] "38G3" "38G4" "38G4" "37G3" ...
#> $ sub_div : chr [1:5144] "24" "24" "24" "24" ...
#> $ density_fle: num [1:5144] 19.26 13.7 1.99 3.89 34.26 ...
#> $ depth : num [1:5144] 8.87 6.87 7.63 5.62 12.87 ...
#> $ year_f : Factor w/ 27 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(d_sub_oxy_q4)
#> tibble [3,453 × 12] (S3: tbl_df/tbl/data.frame)
#> $ density : num [1:3453] 71.71 146.95 864.13 19.48 4.27 ...
#> $ year : int [1:3453] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:3453] 57.5 57.5 54.7 57.1 57.8 ...
#> $ lon : num [1:3453] 17.1 17.6 13.7 18.9 19.5 ...
#> $ quarter : int [1:3453] 4 4 4 4 4 4 4 4 4 4 ...
#> $ haul.id : chr [1:3453] "1993:4:SWE:ARG:FOT:262:31" "1993:4:SWE:ARG:FOT:263:32" "1993:4:GFR:SOL:H20:34:60" "1993:4:SWE:ARG:FOT:256:25" ...
#> $ IDx : chr [1:3453] "1993.4.SWE.77AR.FOT.262.31" "1993.4.SWE.77AR.FOT.263.32" "1993.4.GFR.06S1.H20.34.60" "1993.4.SWE.77AR.FOT.256.25" ...
#> $ ices_rect : chr [1:3453] "43G7" "43G7" "38G3" "43G8" ...
#> $ sub_div : chr [1:3453] "27" "27" "24" "28" ...
#> $ density_fle: num [1:3453] 175.9 13 230.5 25.8 46.5 ...
#> $ depth : num [1:3453] 71.4 86.3 19.9 76.2 86.1 ...
#> $ year_f : Factor w/ 27 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(big_dat_oxy)
#> tibble [8,597 × 5] (S3: tbl_df/tbl/data.frame)
#> $ lon : num [1:8597] 13 14.3 14.2 14 13.1 ...
#> $ lat : num [1:8597] 54.7 54.5 54.5 54.5 54.7 ...
#> $ oxy : num [1:8597] 380 398 398 398 377 ...
#> $ year : chr [1:8597] "1993" "1993" "1993" "1993" ...
#> $ quarter: num [1:8597] 1 1 1 1 1 1 1 1 1 1 ...
# Create an ID for matching the oxygen data with the cpue data
dat$id_oxy <- paste(dat$year, dat$quarter, dat$lon, dat$lat, sep = "_")
big_dat_oxy$id_oxy <- paste(big_dat_oxy$year, big_dat_oxy$quarter, big_dat_oxy$lon, big_dat_oxy$lat, sep = "_")
# Which id's are NOT in the cpue data (dat)?
head(dat$id_oxy, 100)
#> [1] "1992_1_13.0333_54.95" "1992_1_13.5_55.0333" "1992_1_13.9_55.1"
#> [4] "1992_1_13.7333_54.8" "1992_1_13.4167_54.8833" "1992_1_14.8667_54.8833"
#> [7] "1992_1_14.5333_54.6333" "1992_1_15.1_55.6333" "1992_1_15.0833_55.25"
#> [10] "1992_1_15.3_55.1" "1992_1_15.7333_55.4333" "1992_1_15.8833_55.5833"
#> [13] "1992_1_15.5833_55.55" "1992_1_16.0667_55.6833" "1992_1_16.25_55.7833"
#> [16] "1992_1_16.4167_55.85" "1992_1_16.9833_56.1" "1992_1_17.65_56.1167"
#> [19] "1992_1_16.65_55.6333" "1992_1_16.5833_55.5833" "1992_1_16.3333_55.3167"
#> [22] "1992_1_16.2167_55.3" "1992_1_16.05_55.25" "1992_1_15.25_54.8167"
#> [25] "1992_1_15.0333_54.8667" "1992_1_18.3_56.1333" "1992_1_18.3667_56.1833"
#> [28] "1992_1_18.5333_56.15" "1992_1_18.5333_56.2" "1992_1_20.9833_57.2667"
#> [31] "1992_1_20.5833_56.6" "1992_1_20.5833_56.6" "1992_1_20.6167_56.6167"
#> [34] "1992_1_13.0833_54.6667" "1992_1_13.1333_54.7167" "1992_1_14.1667_54.5833"
#> [37] "1992_1_14.25_54.6333" "1992_1_13.3167_54.8" "1992_1_13.4667_54.75"
#> [40] "1992_1_13.85_54.5667" "1992_1_13.8667_54.75" "1992_1_13.6667_54.7333"
#> [43] "1992_1_14.0333_54.8667" "1992_1_13.95_54.9333" "1992_1_13.1333_54.8833"
#> [46] "1992_1_13.1167_54.9333" "1992_1_13.2667_54.9833" "1992_1_13.4333_55.0167"
#> [49] "1992_1_13.75_55.1" "1992_1_14.1667_55.15" "1992_1_14.2667_55.1833"
#> [52] "1992_1_14.3333_55.1" "1992_1_14.3_55.0167" "1992_1_13.5667_55.0333"
#> [55] "1992_1_13.8333_55.1" "1992_1_16.9173_57.3635" "1992_1_17.025_57.4033"
#> [58] "1992_1_14.6833_55.58" "1992_1_17.57_57.4783" "1992_1_17.515_56.1467"
#> [61] "1992_1_18.625_56.4767" "1992_1_18.8767_57.1767" "1992_1_18.8683_57.1017"
#> [64] "1992_1_18.885_57.0683" "1992_1_15.37_55.945" "1992_1_18.9_57.0217"
#> [67] "1992_1_17.7967_56.0966" "1992_1_17.7017_55.855" "1992_1_17.1317_55.9267"
#> [70] "1992_1_16.5233_55.6783" "1992_1_14.4433_55.695" "1992_1_15.9483_55.4533"
#> [73] "1992_1_15.8866_55.7983" "1992_1_19.1617_57.3617" "1992_1_19.5333_57.8167"
#> [76] "1992_1_19.53_57.885" "1992_1_19.4633_58.0566" "1992_1_18.1183_57.8083"
#> [79] "1992_1_17.9117_57.3733" "1992_1_17.915_57.1017" "1992_1_17.965_57.045"
#> [82] "1992_1_17.2983_57.14" "1992_1_16.065_55.8767" "1992_1_14.6483_55.4517"
#> [85] "1992_1_14.4457_55.456" "1992_1_13.2_55.2083" "1992_1_13.5917_55.21"
#> [88] "1992_1_13.25_54.97" "1992_1_13.9233_55.0133" "1992_4_13.45_55.0167"
#> [91] "1992_4_13.1_54.9167" "1992_4_13.9667_54.9333" "1992_4_14.35_55.1"
#> [94] "1992_4_14.1667_55.1333" "1992_4_13.9_54.5" "1992_4_14.2_54.5"
#> [97] "1992_4_14.2833_55.1833" "1992_4_13.8_54.5167" "1992_4_14.3667_54.5167"
#> [100] "1992_4_13.8833_54.5667"
head(big_dat_oxy$id_oxy, 100)
#> [1] "1993_1_13_54.6833" "1993_1_14.2667_54.5167" "1993_1_14.15_54.5167"
#> [4] "1993_1_13.9833_54.4833" "1993_1_13.0833_54.7333" "1993_1_13.1833_54.7"
#> [7] "1993_1_13.8_54.55" "1993_1_14.1167_54.7" "1993_1_14.0833_54.5833"
#> [10] "1993_1_13.8667_54.5833" "1993_1_14.15_54.6333" "1993_1_13.3667_54.7333"
#> [13] "1993_1_13.5167_54.7333" "1993_1_13.6333_54.6833" "1993_1_13.7333_54.6667"
#> [16] "1993_1_13.85_54.7" "1993_1_13.6667_54.7667" "1993_1_14.0167_54.85"
#> [19] "1993_1_13.8833_54.9667" "1993_1_13.6167_54.9167" "1993_1_13.2167_54.8667"
#> [22] "1993_1_13.1_54.9333" "1993_1_13.3167_54.9833" "1993_1_13.4333_55.0333"
#> [25] "1993_1_13.75_55.0667" "1993_1_14.1667_55.1333" "1993_1_14.2667_55.1667"
#> [28] "1993_1_14.2833_55.0667" "1993_1_14.2333_55" "1993_1_13.6167_55.05"
#> [31] "1993_1_13.8167_55.0833" "1993_1_16.6_54.8667" "1993_1_15.1167_54.3"
#> [34] "1993_1_16.5167_54.8333" "1993_1_15.0167_54.4333" "1993_1_16.6333_54.85"
#> [37] "1993_1_15.7667_54.3667" "1993_1_16.0167_54.4333" "1993_1_15.3333_55.9333"
#> [40] "1993_1_15.7167_54.4333" "1993_1_16.2167_54.9167" "1993_1_16.3_55.8333"
#> [43] "1993_1_15.95_54.7833" "1993_1_17.6167_56.0833" "1993_1_15.6_55.9167"
#> [46] "1993_1_15.5833_54.6167" "1993_1_16.2333_55.7833" "1993_1_17.75_56.1333"
#> [49] "1993_1_15.1167_55.6833" "1993_1_16.4333_55.45" "1993_1_14.8667_55.6167"
#> [52] "1993_1_15.6_54.7667" "1993_1_16.35_55.3667" "1993_1_15.8833_55.6"
#> [55] "1993_1_15.7_55.6" "1993_1_17.35_55.2667" "1993_1_16.2_55.3"
#> [58] "1993_1_15.1667_55.4333" "1993_1_17.55_55.25" "1993_1_16.05_55.1333"
#> [61] "1993_1_14.5143_55.6712" "1993_1_16.611_55.6187" "1993_1_16.8935_55.785"
#> [64] "1993_1_14.7333_55.455" "1993_1_14.5483_55.465" "1993_1_13.9913_54.9983"
#> [67] "1993_1_13.6493_55.213" "1993_1_13.32_54.935" "1993_1_13.2767_55.21"
#> [70] "1993_1_15.4393_55.8218" "1993_1_15.46_55.9933" "1993_1_19.5368_57.8885"
#> [73] "1993_1_16.1217_55.795" "1993_1_16.085_55.8733" "1993_1_17.0855_55.8667"
#> [76] "1993_1_17.1323_55.9248" "1993_1_17.7367_55.8015" "1993_1_17.7188_56.0352"
#> [79] "1993_1_16.9745_56.445" "1993_1_16.8225_56.5178" "1993_1_17.94_57.004"
#> [82] "1993_1_14.7172_55.5687" "1993_1_17.9145_57.066" "1993_1_16.9133_57.3685"
#> [85] "1993_1_17.0157_57.5172" "1993_1_17.5322_57.4502" "1993_1_18.123_57.821"
#> [88] "1993_1_18.3128_57.7555" "1993_1_16.0507_55.3282" "1993_1_15.3205_55.412"
#> [91] "1993_1_19.5493_57.8677" "1993_1_19.0568_57.5685" "1993_1_19.1942_57.3825"
#> [94] "1993_1_18.8761_57.1808" "1993_1_18.8363_57.085" "1993_1_18.9005_57.089"
#> [97] "1993_1_18.4828_56.2562" "1993_1_18.3732_55.5878" "1993_1_16.9951_55.2882"
#> [100] "1993_1_17.5_55"
tail(dat$id_oxy, 100)
#> [1] "2018_4_20.37_57.0317" "2018_4_20.3383_56.6" "2018_4_20.1317_56.4817"
#> [4] "2018_4_20.0833_56.4333" "2018_4_19.7067_56.38" "2018_4_19.525_56.3"
#> [7] "2018_4_19.4933_58.035" "2018_4_19.5305_57.8297" "2018_4_18.1183_57.79"
#> [10] "2018_4_16.0635_55.1584" "2018_4_16.053_55.1374" "2018_4_15.7322_55.0559"
#> [13] "2018_4_17.7017_55.78" "2018_4_20.5933_57.34" "2018_4_15.4397_55.7654"
#> [16] "2018_4_14.7118_55.6254" "2018_4_14.8696_55.623" "2018_4_15.255_55.5437"
#> [19] "2018_4_16.1757_55.5421" "2018_4_15.5875_55.5354" "2018_4_14.9434_55.5179"
#> [22] "2018_4_15.5546_55.466" "2018_4_15.9157_55.4343" "2018_4_15.2662_55.3745"
#> [25] "2018_4_16.0234_55.3034" "2018_4_15.9613_55.2382" "2018_4_19.4517_56.2"
#> [28] "2018_4_20.2267_55.7667" "2018_4_20.06_55.735" "2018_4_19.9767_55.6683"
#> [31] "2018_4_20.275_55.6583" "2018_4_20.1567_55.6433" "2018_4_18.9933_55.5083"
#> [34] "2019_1_18.5467_56.3017" "2019_1_19.4592_56.199" "2019_1_19.5188_56.28"
#> [37] "2019_1_19.6268_56.3762" "2019_1_19.8065_56.4432" "2019_1_17.9563_57.0228"
#> [40] "2019_1_17.9102_57.074" "2019_1_17.2842_57.0972" "2019_1_17.4182_57.3255"
#> [43] "2019_1_19.1467_57.3458" "2019_1_18.3733_55.605" "2019_1_19.083_57.3555"
#> [46] "2019_1_17.5442_57.4563" "2019_1_19.2515_57.4993" "2019_1_18.5867_56.385"
#> [49] "2019_1_17.0822_57.6312" "2019_1_19.4818_57.7923" "2019_1_15.73_54.3783"
#> [52] "2019_4_19.1013_57.2805" "2019_4_17.8862_57.3405" "2019_4_16.997_57.4457"
#> [55] "2019_4_17.0787_57.6348" "2019_4_18.3157_57.755" "2019_4_18.1202_57.7905"
#> [58] "2019_4_19.5262_57.8475" "2019_4_19.5033_56.2167" "2019_4_19.4908_56.2172"
#> [61] "2019_4_18.6763_56.3202" "2019_4_18.6765_56.4617" "2020_1_18.8967_54.55"
#> [64] "2020_1_18.7983_55.3583" "2020_1_17.0433_55.3833" "2020_1_19.049_57.1968"
#> [67] "2020_1_18.828_57.1662" "2020_1_18.9622_57.1165" "2020_1_19.439_58.075"
#> [70] "2020_1_19.4817_57.7677" "2020_1_17.7363_57.4597" "2020_1_17.9093_57.0747"
#> [73] "2020_1_18.8312_57.0655" "2020_1_18.8742_57.0632" "2020_1_19.11_57.321"
#> [76] "2020_1_19.2567_54.3883" "2020_4_21.4283_57.4983" "2020_4_20.5433_57.0467"
#> [79] "2020_4_20.08_56.3833" "2020_4_18.015_55.69" "2020_4_16.9165_57.3647"
#> [82] "2020_4_19.2267_57.3361" "2020_4_19.1085_57.319" "2020_4_17.4146_57.2805"
#> [85] "2020_4_19.0668_57.2496" "2020_4_18.9076_57.089" "2020_4_19.4891_58.0354"
#> [88] "2020_4_19.5044_57.876" "2020_4_18.1196_57.7804" "2020_4_19.4807_57.7685"
#> [91] "2020_4_19.3769_57.6885" "2020_4_17.0977_57.6175" "2020_4_17.5616_57.4828"
#> [94] "2020_4_17.0912_57.4622" "2020_4_17.9167_57.0532" "2020_4_17.9509_56.9956"
#> [97] "2020_4_17.184_56.9584" "2020_4_16.9939_56.7256" "2020_4_16.85_56.5655"
#> [100] "2020_4_19.439_58.0746"
tail(big_dat_oxy$id_oxy, 100)
#> [1] "2018_4_15.9613_55.2382" "2018_4_19.4517_56.2" "2018_4_20.2267_55.7667"
#> [4] "2018_4_20.06_55.735" "2018_4_19.9767_55.6683" "2018_4_20.275_55.6583"
#> [7] "2018_4_20.1567_55.6433" "2018_4_18.9933_55.5083" "2019_4_17.3133_55.3233"
#> [10] "2019_4_14.529_55.4627" "2019_4_18.6817_54.785" "2019_4_18.7083_54.7833"
#> [13] "2019_4_18.75_54.7317" "2019_4_18.9583_54.565" "2019_4_14.6418_55.4718"
#> [16] "2019_4_18.535_54.8133" "2019_4_17.2767_55.3117" "2019_4_14.3668_55.696"
#> [19] "2019_4_15.955_54.75" "2019_4_16.915_57.365" "2019_4_14.3798_55.7077"
#> [22] "2019_4_17.3167_55.2383" "2019_4_14.4607_55.6847" "2019_4_14.51_55.6878"
#> [25] "2019_4_14.374_55.7028" "2019_4_17.9512_56.992" "2019_4_19.1683_54.6"
#> [28] "2019_4_17.9208_57.0495" "2019_4_19.5073_57.8783" "2019_4_19.31_54.6267"
#> [31] "2019_4_18.8628_57.1762" "2019_4_17.1777_56.944" "2019_4_19.2217_54.6333"
#> [34] "2019_4_19.3283_54.45" "2019_4_19.3033_54.4283" "2019_4_19.305_54.4167"
#> [37] "2019_4_19.2983_54.4" "2019_4_16.885_54.7417" "2019_4_16.8333_54.7483"
#> [40] "2019_4_16.6767_54.7017" "2019_4_16.4183_54.6567" "2019_4_16.045_54.83"
#> [43] "2019_4_16.0683_54.6317" "2019_4_15.8267_54.4667" "2019_4_18.961_57.2812"
#> [46] "2019_4_16.035_54.4083" "2019_4_16.0567_54.4383" "2019_4_15.7667_54.38"
#> [49] "2019_4_15.6433_54.3817" "2019_4_16.21_55.045" "2019_4_17.4917_54.8517"
#> [52] "2019_4_17.4233_54.955" "2019_4_18.4888_56.3935" "2019_4_17.4533_54.995"
#> [55] "2019_4_16.705_54.8783" "2019_4_16.8033_55.42" "2019_4_17.2667_55.4567"
#> [58] "2019_4_17.3933_55.385" "2019_4_18.1817_54.9017" "2019_4_18.535_54.985"
#> [61] "2019_4_18.6372_55.9193" "2019_4_18.58_54.8517" "2019_4_18.4312_56.2255"
#> [64] "2019_4_18.5417_54.9817" "2019_4_18.6_54.9133" "2019_4_18.4437_56.3492"
#> [67] "2019_4_18.635_54.8883" "2019_4_18.655_54.895" "2019_4_19.1783_54.4317"
#> [70] "2019_4_19.0633_54.4433" "2019_4_19.0617_54.4367" "2019_4_16.9977_56.6928"
#> [73] "2019_4_19.0417_54.4017" "2019_4_19.0167_54.405" "2019_4_15.5732_55.8533"
#> [76] "2019_4_18.99_54.4017" "2019_4_19.04_54.94" "2019_4_15.2227_55.7868"
#> [79] "2019_4_18.8167_54.9733" "2019_4_18.8_54.965" "2019_4_18.645_55.1767"
#> [82] "2019_4_18.33_55.465" "2019_4_14.4635_55.6833" "2019_4_18.325_55.4583"
#> [85] "2019_4_18.7733_55.8883" "2019_4_18.0533_55.6317" "2019_4_17.5967_55.6167"
#> [88] "2019_4_17.5533_55.3933" "2019_4_17.76_55.395" "2019_4_19.1013_57.2805"
#> [91] "2019_4_17.8862_57.3405" "2019_4_16.997_57.4457" "2019_4_17.0787_57.6348"
#> [94] "2019_4_18.3157_57.755" "2019_4_18.1202_57.7905" "2019_4_19.5262_57.8475"
#> [97] "2019_4_19.5033_56.2167" "2019_4_19.4908_56.2172" "2019_4_18.6763_56.3202"
#> [100] "2019_4_18.6765_56.4617"
#dat %>% group_by(year) %>% summarise(n = n()) %>% as.data.frame()
ids <- dat$id_oxy[!dat$id_oxy %in% c(big_dat_oxy$id_oxy)]
dat %>% filter(id_oxy %in% ids)
#> filter: removed 8,597 rows (96%), 342 rows remaining
#> # A tibble: 342 × 12
#> density year lat lon quarter haul.id IDx ices_…¹ sub_div densi…² depth
#> <dbl> <int> <dbl> <dbl> <int> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 25.0 1992 55.0 13.0 1 1992:1… 1992… 38G3 24 13.1 22.3
#> 2 96.4 1992 55.0 13.5 1 1992:1… 1992… 39G3 24 117. 30.9
#> 3 141. 1992 55.1 13.9 1 1992:1… 1992… 39G3 24 267. 31.9
#> 4 102. 1992 54.8 13.7 1 1992:1… 1992… 38G3 24 57.2 27.9
#> 5 133. 1992 54.9 13.4 1 1992:1… 1992… 38G3 24 80.6 31.9
#> 6 46.4 1992 54.9 14.9 1 1992:1… 1992… 38G4 24 21.3 39.5
#> 7 45.4 1992 54.6 14.5 1 1992:1… 1992… 38G4 24 17.8 21.6
#> 8 733. 1992 55.6 15.1 1 1992:1… 1992… 40G5 25 790. 71.4
#> 9 133. 1992 55.2 15.1 1 1992:1… 1992… 39G5 25 141. 61.9
#> 10 200. 1992 55.1 15.3 1 1992:1… 1992… 39G5 25 286. 69.9
#> # … with 332 more rows, 1 more variable: id_oxy <chr>, and abbreviated variable
#> # names ¹​ices_rect, ²​density_fle
# Select only the columns we want to merge
big_dat_sub_oxy <- big_dat_oxy %>% dplyr::select(id_oxy, oxy)
# Remove duplicate ID (one oxy value per id)
big_dat_sub_oxy %>% group_by(id_oxy) %>% mutate(n = n()) %>% arrange(desc(n))
#> group_by: one grouping variable (id_oxy)
#> mutate (grouped): new variable 'n' (integer) with 4 unique values and 0% NA
#> # A tibble: 8,597 × 3
#> # Groups: id_oxy [8,576]
#> id_oxy oxy n
#> <chr> <dbl> <int>
#> 1 1999_1_15.3667_54.5667 298. 4
#> 2 1999_1_15.3667_54.5667 298. 4
#> 3 1999_1_15.3667_54.5667 298. 4
#> 4 1999_1_15.3667_54.5667 298. 4
#> 5 1999_4_15.2833_55.8 184. 3
#> 6 1999_4_15.2833_55.8 184. 3
#> 7 1999_4_15.2833_55.8 184. 3
#> 8 1995_1_18.7833_54.6833 385. 2
#> 9 1995_1_18.7833_54.6833 385. 2
#> 10 1995_1_18.65_54.8667 384. 2
#> # … with 8,587 more rows
big_dat_sub_oxy2 <- big_dat_sub_oxy %>% distinct(id_oxy, .keep_all = TRUE)
#> distinct: removed 21 rows (<1%), 8,576 rows remaining
# Join the data with raster-derived oxygen with the full cpue data
nrow(dat)
#> [1] 8939
dat <- left_join(dat, big_dat_sub_oxy2, by = "id_oxy")
#> left_join: added one column (oxy)
#> > rows only in x 342
#> > rows only in y ( 0)
#> > matched rows 8,597
#> > =======
#> > rows total 8,939
nrow(dat)
#> [1] 8939
# Now the unit of oxygen is mmol/m3. I want it to be ml/L. The original model is in unit ml/L
# and it's been converted by the data host. Since it was converted without accounting for
# pressure or temperature, I can simply use the following conversion factor:
# 1 ml/l = 103/22.391 = 44.661 μmol/l -> 1 ml/l = 0.044661 mmol/l = 44.661 mmol/m^3 -> 0.0223909 ml/l = 1mmol/m^3
# https://ocean.ices.dk/tools/unitconversion.aspx
dat$oxy <- dat$oxy * 0.0223909
# Drop NA oxygen
dat <- dat %>% drop_na(oxy)
#> drop_na: removed 343 rows (4%), 8,596 rows remaining
sort(unique(dat$year))
#> [1] 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
#> [16] 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019Temperature
# Open the netCDF file
ncin <- nc_open("data/NEMO_Nordic_SCOBI/dataset-reanalysis-nemo-monthlymeans_1608127623694.nc")
print(ncin)
#> File data/NEMO_Nordic_SCOBI/dataset-reanalysis-nemo-monthlymeans_1608127623694.nc (NC_FORMAT_CLASSIC):
#>
#> 1 variables (excluding dimension variables):
#> float bottomT[longitude,latitude,time]
#> standard_name: sea_water_potential_temperature_at_sea_floor
#> units: degrees_C
#> long_name: Sea floor potential temperature
#> missing_value: NaN
#> _FillValue: NaN
#> _ChunkSizes: 1
#> _ChunkSizes: 523
#> _ChunkSizes: 383
#>
#> 3 dimensions:
#> time Size:324
#> axis: T
#> long_name: Validity time
#> standard_name: time
#> units: days since 1950-01-01 00:00:00
#> calendar: gregorian
#> _ChunkSizes: 512
#> _CoordinateAxisType: Time
#> valid_min: 15721.5
#> valid_max: 25551.5
#> latitude Size:523
#> axis: Y
#> standard_name: latitude
#> long_name: latitude
#> units: degrees_north
#> _CoordinateAxisType: Lat
#> valid_min: 48.49169921875
#> valid_max: 65.8914184570312
#> longitude Size:383
#> standard_name: longitude
#> long_name: longitude
#> units: degrees_east
#> axis: X
#> _CoordinateAxisType: Lon
#> valid_min: 9.01375484466553
#> valid_max: 30.2357654571533
#>
#> 24 global attributes:
#> references: http://www.smhi.se
#> institution: Swedish Meterological and Hydrological Institute
#> history: See source and creation_date attributees
#> Conventions: CF-1.5
#> contact: servicedesk_cmems@mercator-ocean.eu
#> comment: Provided by SMHI as a Copernicus Marine Environment Monitoring Service production unit
#> bullentin_type: reanalysis
#> cmems_product_id: BALTICSEA_REANALYSIS_PHY_003_011
#> title: CMEMS V4 Reanalysis: NEMO model 3D fields (monthly means)
#> FROM_ORIGINAL_FILE__easternmost_longitude: 30.2357654571533
#> FROM_ORIGINAL_FILE__northernmost_latitude: 65.8914184570312
#> FROM_ORIGINAL_FILE__westernmost_longitude: 9.01375484466553
#> FROM_ORIGINAL_FILE__southernmost_latitude: 48.49169921875
#> shallowest_depth: 1.50136542320251
#> deepest_depth: 711.059204101562
#> source: SMHI reanalysis run NORDIC-NS2_1d_20191201_20191202
#> file_quality_index: 1
#> creation_date: 2020-11-16 UTC
#> bullentin_date: 20191201
#> start_date: 2019-12-01 UTC
#> stop_date: 2019-12-01 UTC
#> start_time: 00:00 UTC
#> stop_time: 00:00 UTC
#> _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
# Get longitude and latitude
lon <- ncvar_get(ncin,"longitude")
nlon <- dim(lon)
head(lon)
#> [1] 9.013755 9.069310 9.124865 9.180420 9.235975 9.291530
lat <- ncvar_get(ncin,"latitude")
nlat <- dim(lat)
head(lat)
#> [1] 48.49170 48.52503 48.55836 48.59170 48.62503 48.65836
# Get time
time <- ncvar_get(ncin,"time")
time
#> [1] 15721.5 15751.0 15780.5 15811.0 15841.5 15872.0 15902.5 15933.5 15964.0
#> [10] 15994.5 16025.0 16055.5 16086.5 16116.0 16145.5 16176.0 16206.5 16237.0
#> [19] 16267.5 16298.5 16329.0 16359.5 16390.0 16420.5 16451.5 16481.0 16510.5
#> [28] 16541.0 16571.5 16602.0 16632.5 16663.5 16694.0 16724.5 16755.0 16785.5
#> [37] 16816.5 16846.5 16876.5 16907.0 16937.5 16968.0 16998.5 17029.5 17060.0
#> [46] 17090.5 17121.0 17151.5 17182.5 17212.0 17241.5 17272.0 17302.5 17333.0
#> [55] 17363.5 17394.5 17425.0 17455.5 17486.0 17516.5 17547.5 17577.0 17606.5
#> [64] 17637.0 17667.5 17698.0 17728.5 17759.5 17790.0 17820.5 17851.0 17881.5
#> [73] 17912.5 17942.0 17971.5 18002.0 18032.5 18063.0 18093.5 18124.5 18155.0
#> [82] 18185.5 18216.0 18246.5 18277.5 18307.5 18337.5 18368.0 18398.5 18429.0
#> [91] 18459.5 18490.5 18521.0 18551.5 18582.0 18612.5 18643.5 18673.0 18702.5
#> [100] 18733.0 18763.5 18794.0 18824.5 18855.5 18886.0 18916.5 18947.0 18977.5
#> [109] 19008.5 19038.0 19067.5 19098.0 19128.5 19159.0 19189.5 19220.5 19251.0
#> [118] 19281.5 19312.0 19342.5 19373.5 19403.0 19432.5 19463.0 19493.5 19524.0
#> [127] 19554.5 19585.5 19616.0 19646.5 19677.0 19707.5 19738.5 19768.5 19798.5
#> [136] 19829.0 19859.5 19890.0 19920.5 19951.5 19982.0 20012.5 20043.0 20073.5
#> [145] 20104.5 20134.0 20163.5 20194.0 20224.5 20255.0 20285.5 20316.5 20347.0
#> [154] 20377.5 20408.0 20438.5 20469.5 20499.0 20528.5 20559.0 20589.5 20620.0
#> [163] 20650.5 20681.5 20712.0 20742.5 20773.0 20803.5 20834.5 20864.0 20893.5
#> [172] 20924.0 20954.5 20985.0 21015.5 21046.5 21077.0 21107.5 21138.0 21168.5
#> [181] 21199.5 21229.5 21259.5 21290.0 21320.5 21351.0 21381.5 21412.5 21443.0
#> [190] 21473.5 21504.0 21534.5 21565.5 21595.0 21624.5 21655.0 21685.5 21716.0
#> [199] 21746.5 21777.5 21808.0 21838.5 21869.0 21899.5 21930.5 21960.0 21989.5
#> [208] 22020.0 22050.5 22081.0 22111.5 22142.5 22173.0 22203.5 22234.0 22264.5
#> [217] 22295.5 22325.0 22354.5 22385.0 22415.5 22446.0 22476.5 22507.5 22538.0
#> [226] 22568.5 22599.0 22629.5 22660.5 22690.5 22720.5 22751.0 22781.5 22812.0
#> [235] 22842.5 22873.5 22904.0 22934.5 22965.0 22995.5 23026.5 23056.0 23085.5
#> [244] 23116.0 23146.5 23177.0 23207.5 23238.5 23269.0 23299.5 23330.0 23360.5
#> [253] 23391.5 23421.0 23450.5 23481.0 23511.5 23542.0 23572.5 23603.5 23634.0
#> [262] 23664.5 23695.0 23725.5 23756.5 23786.0 23815.5 23846.0 23876.5 23907.0
#> [271] 23937.5 23968.5 23999.0 24029.5 24060.0 24090.5 24121.5 24151.5 24181.5
#> [280] 24212.0 24242.5 24273.0 24303.5 24334.5 24365.0 24395.5 24426.0 24456.5
#> [289] 24487.5 24517.0 24546.5 24577.0 24607.5 24638.0 24668.5 24699.5 24730.0
#> [298] 24760.5 24791.0 24821.5 24852.5 24882.0 24911.5 24942.0 24972.5 25003.0
#> [307] 25033.5 25064.5 25095.0 25125.5 25156.0 25186.5 25217.5 25247.0 25276.5
#> [316] 25307.0 25337.5 25368.0 25398.5 25429.5 25460.0 25490.5 25521.0 25551.5
tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt
#> [1] 324
tunits
#> $hasatt
#> [1] TRUE
#>
#> $value
#> [1] "days since 1950-01-01 00:00:00"
# Get temperature
dname <- "bottomT"
temp_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(temp_array)
#> [1] 383 523 324
# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")
# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])
# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))
# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)
# Replace netCDF fill values with NA's
temp_array[temp_array == fillvalue$value] <- NA
# We only use Quarter 4 in this analysis, so now we want to loop through each time step,
# and if it is a good month save it as a raster.
# First get the index of months that correspond to Q4
months
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1
#> [26] 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2
#> [51] 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3
#> [76] 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4
#> [101] 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5
#> [126] 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6
#> [151] 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7
#> [176] 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8
#> [201] 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9
#> [226] 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10
#> [251] 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11
#> [276] 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
#> [301] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
index_keep <- which(months > 9)
# Quarter 4 by keeping months in index_keep
temp_q4 <- temp_array[, , index_keep]
months_keep <- months[index_keep]
years_keep <- years[index_keep]
# Now we have an array with only Q4 data...
# We need to now calculate the average within a year.
# Get a sequence that takes every third value between 1: number of months (length)
loop_seq <- seq(1, dim(temp_q4)[3], by = 3)
# Create objects that will hold data
dlist <- list()
temp_10 <- c()
temp_11 <- c()
temp_12 <- c()
temp_ave <- c()
# Loop through the vector sequence with every third value, then take the average of
# three consecutive months (i.e. q4)
for(i in loop_seq) {
temp_10 <- temp_q4[, , (i)]
temp_11 <- temp_q4[, , (i + 1)]
temp_12 <- temp_q4[, , (i + 2)]
temp_ave <- (temp_10 + temp_11 + temp_12) / 3
list_pos <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
dlist[[list_pos]] <- temp_ave
}
# Now name the lists with the year:
names(dlist) <- unique(years_keep)
# Now I need to make a loop where I extract the raster value for each year...
# The cpue data is called dat so far in this script
# Filter years in the cpue data frame to only have the years I have temperature for
d_sub_temp <- dat %>% filter(year %in% names(dlist)) %>% droplevels()
#> filter: no rows removed
# Create data holding object
data_list <- list()
# ... And for the temperature raster
raster_list <- list()
# Create factor year for indexing the list in the loop
d_sub_temp$year_f <- as.factor(d_sub_temp$year)
# Loop through each year and extract raster values for the cpue data points
for(i in unique(d_sub_temp$year_f)) {
# Subset a year
temp_slice <- dlist[[i]]
# Create raster for that year (i)
r <- raster(t(temp_slice), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
# Flip...
r <- flip(r, direction = 'y')
#plot(r, main = i)
# Filter the same year (i) in the cpue data and select only coordinates
d_slice <- d_sub_temp %>% filter(year_f == i) %>% dplyr::select(lon, lat)
# Make into a SpatialPoints object
data_sp <- SpatialPoints(d_slice)
# Extract raster value (temperature)
rasValue <- raster::extract(r, data_sp, method = "bilinear")
# Now we want to plot the results of the raster extractions by plotting the cpue
# data points over a raster and saving it for each year.
# Make the SpatialPoints object into a raster again (for pl)
df <- as.data.frame(data_sp)
# Add in the raster value in the df holding the coordinates for the cpue data
d_slice$temp <- rasValue
# Add in which year
d_slice$year <- i
# Create a index for the data last where we store all years (because our loop index
# i is not continuous, we can't use it directly)
index <- as.numeric(d_slice$year)[1] - 1992
# Add each years' data in the list
data_list[[index]] <- d_slice
# Save to check each year is ok! First convert the raster to points for plotting
# (so that we can use ggplot)
map.p <- rasterToPoints(r)
# Make the points a dataframe for ggplot
df_rast <- data.frame(map.p)
# Rename y-variable and add year
df_rast <- df_rast %>% rename("temp" = "layer") %>% mutate(year = i)
# Add each years' raster data frame in the list
raster_list[[index]] <- df_rast
# Make appropriate column headings
colnames(df_rast) <- c("Longitude", "Latitude", "temp")
# Now make the map
ggplot(data = df_rast, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = temp)) +
geom_point(data = d_slice, aes(x = lon, y = lat, fill = temp),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(min(dat$lon), max(dat$lon)),
ylim = c(min(dat$lat), max(dat$lat))) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(2, 17)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(2, 17))
ggsave(paste("figures/supp/density/density_temp_rasters/", i,".png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
}
#> filter: removed 8,434 rows (98%), 162 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,391 rows (98%), 205 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,389 rows (98%), 207 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,400 rows (98%), 196 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,377 rows (97%), 219 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,353 rows (97%), 243 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,331 rows (97%), 265 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,397 rows (98%), 199 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,274 rows (96%), 322 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,337 rows (97%), 259 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,357 rows (97%), 239 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,335 rows (97%), 261 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,290 rows (96%), 306 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,258 rows (96%), 338 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,199 rows (95%), 397 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,199 rows (95%), 397 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,190 rows (95%), 406 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,194 rows (95%), 402 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,173 rows (95%), 423 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,240 rows (96%), 356 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,180 rows (95%), 416 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,212 rows (96%), 384 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,193 rows (95%), 403 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,151 rows (95%), 445 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,126 rows (95%), 470 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,130 rows (95%), 466 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 8,386 rows (98%), 210 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
# Now create a data frame from the list of all annual values
big_dat_temp <- dplyr::bind_rows(data_list)
big_raster_dat_temp <- dplyr::bind_rows(raster_list)
big_dat_temp %>% drop_na(temp) %>% summarise(max = max(temp))
#> drop_na: no rows removed
#> summarise: now one row and one column, ungrouped
#> # A tibble: 1 × 1
#> max
#> <dbl>
#> 1 14.4
big_dat_temp %>% drop_na(temp) %>% summarise(min = min(temp))
#> drop_na: no rows removed
#> summarise: now one row and one column, ungrouped
#> # A tibble: 1 × 1
#> min
#> <dbl>
#> 1 2.96
# Plot data, looks like there's big inter-annual variation but a positive trend
big_raster_dat_temp %>%
group_by(year) %>%
drop_na(temp) %>%
summarise(mean_temp = mean(temp)) %>%
mutate(year_num = as.numeric(year)) %>%
ggplot(., aes(year_num, mean_temp)) +
geom_point(size = 2) +
stat_smooth(method = "lm")
#> group_by: one grouping variable (year)
#> drop_na (grouped): no rows removed
#> summarise: now 27 rows and 2 columns, ungrouped
#> mutate: new variable 'year_num' (double) with 27 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'
# Now add in the new temperature column in the original data:
str(d_sub_temp)
#> tibble [8,596 × 14] (S3: tbl_df/tbl/data.frame)
#> $ density : num [1:8596] 158.15 10.33 5.26 19.55 812.16 ...
#> $ year : int [1:8596] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:8596] 54.7 54.5 54.5 54.5 54.7 ...
#> $ lon : num [1:8596] 13 14.3 14.2 14 13.1 ...
#> $ quarter : int [1:8596] 1 1 1 1 1 1 1 1 1 1 ...
#> $ haul.id : chr [1:8596] "1993:1:GFR:SOL:H20:21:1" "1993:1:GFR:SOL:H20:22:32" "1993:1:GFR:SOL:H20:23:31" "1993:1:GFR:SOL:H20:24:30" ...
#> $ IDx : chr [1:8596] "1993.1.GFR.06S1.H20.21.1" "1993.1.GFR.06S1.H20.22.32" "1993.1.GFR.06S1.H20.23.31" "1993.1.GFR.06S1.H20.24.30" ...
#> $ ices_rect : chr [1:8596] "38G3" "38G4" "38G4" "37G3" ...
#> $ sub_div : chr [1:8596] "24" "24" "24" "24" ...
#> $ density_fle: num [1:8596] 19.26 13.7 1.99 3.89 34.26 ...
#> $ depth : num [1:8596] 8.87 6.87 7.63 5.62 12.87 ...
#> $ id_oxy : chr [1:8596] "1993_1_13_54.6833" "1993_1_14.2667_54.5167" "1993_1_14.15_54.5167" "1993_1_13.9833_54.4833" ...
#> $ oxy : num [1:8596] 8.52 8.91 8.91 8.9 8.45 ...
#> $ year_f : Factor w/ 27 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(big_dat_temp)
#> tibble [8,596 × 4] (S3: tbl_df/tbl/data.frame)
#> $ lon : num [1:8596] 13 14.3 14.2 14 13.1 ...
#> $ lat : num [1:8596] 54.7 54.5 54.5 54.5 54.7 ...
#> $ temp: num [1:8596] 7.92 7.4 7.4 7.35 8.12 ...
#> $ year: chr [1:8596] "1993" "1993" "1993" "1993" ...
# Create an ID for matching the temperature data with the cpue data
dat$id_temp <- paste(dat$year, dat$lon, dat$lat, sep = "_")
big_dat_temp$id_temp <- paste(big_dat_temp$year, big_dat_temp$lon, big_dat_temp$lat, sep = "_")
# Which id's are not in the cpue data (dat)? (It's because I don't have those years, not about the location)
ids <- dat$id_temp[!dat$id_temp %in% c(big_dat_temp$id_temp)]
unique(ids)
#> character(0)
# Select only the columns we want to merge
big_dat_sub_temp <- big_dat_temp %>% dplyr::select(id_temp, temp)
# Remove duplicate ID (one temp value per id)
big_dat_sub_temp2 <- big_dat_sub_temp %>% distinct(id_temp, .keep_all = TRUE)
#> distinct: removed 88 rows (1%), 8,508 rows remaining
# Join the data with raster-derived oxygen with the full cpue data
dat <- left_join(dat, big_dat_sub_temp2, by = "id_temp")
#> left_join: added one column (temp)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 8,596
#> > =======
#> > rows total 8,596
colnames(dat)
#> [1] "density" "year" "lat" "lon" "quarter"
#> [6] "haul.id" "IDx" "ices_rect" "sub_div" "density_fle"
#> [11] "depth" "id_oxy" "oxy" "id_temp" "temp"
dat <- dat %>% dplyr::select(-id_temp, -id_oxy)
# Drop NA temp
dat <- dat %>% drop_na(temp)
#> drop_na: no rows removed
# Now, if quarter == 1 in dat, temperature should be NA beucase I haven't done temperature for q1 here (see comment in the description of this script)
sort(unique(filter(dat, quarter == 1)$temp))
#> filter: removed 3,452 rows (40%), 5,144 rows remaining
#> [1] 2.958224 2.977907 2.993523 3.223797 3.256330 3.374446 3.385705
#> [8] 3.476515 3.549009 3.578635 3.609479 3.612852 3.614922 3.617384
#> [15] 3.617421 3.636575 3.636666 3.639292 3.652544 3.710982 3.719994
#> [22] 3.747388 3.748731 3.749818 3.778201 3.792435 3.797669 3.798714
#> [29] 3.816328 3.832944 3.834569 3.836870 3.844881 3.857099 3.870043
#> [36] 3.875435 3.877317 3.899827 3.911177 3.937457 3.950228 3.962593
#> [43] 3.969385 3.971485 3.982919 3.989504 3.994432 3.997744 3.998825
#> [50] 4.004031 4.014246 4.014594 4.017230 4.026310 4.028686 4.031924
#> [57] 4.044921 4.046828 4.055898 4.079192 4.079518 4.082264 4.090783
#> [64] 4.096813 4.097252 4.097594 4.097764 4.104354 4.105478 4.105582
#> [71] 4.114230 4.118768 4.122777 4.125894 4.126760 4.126840 4.132259
#> [78] 4.133278 4.137680 4.143962 4.147805 4.148928 4.160384 4.162132
#> [85] 4.164441 4.169403 4.171877 4.172824 4.173273 4.176000 4.181066
#> [92] 4.189746 4.191610 4.194535 4.199857 4.200024 4.200540 4.200954
#> [99] 4.202446 4.203196 4.203223 4.207467 4.207693 4.208196 4.209729
#> [106] 4.211332 4.218529 4.218883 4.221886 4.233080 4.233312 4.233783
#> [113] 4.234293 4.234752 4.241692 4.246612 4.248327 4.250917 4.251680
#> [120] 4.253088 4.259002 4.262193 4.272968 4.276708 4.278452 4.283557
#> [127] 4.285990 4.287835 4.294793 4.296268 4.303352 4.309692 4.310034
#> [134] 4.313739 4.317819 4.324457 4.325772 4.331237 4.336834 4.341463
#> [141] 4.343214 4.344606 4.345228 4.348113 4.351997 4.353914 4.354610
#> [148] 4.356189 4.359114 4.359272 4.362440 4.367089 4.367374 4.372076
#> [155] 4.374164 4.376568 4.376785 4.377234 4.377676 4.379583 4.385276
#> [162] 4.386055 4.386161 4.387650 4.393044 4.396782 4.397075 4.404854
#> [169] 4.411055 4.411690 4.415215 4.415274 4.417570 4.417683 4.420362
#> [176] 4.422089 4.424385 4.426818 4.428303 4.435161 4.437474 4.440338
#> [183] 4.440590 4.440610 4.444174 4.444708 4.448224 4.448498 4.449073
#> [190] 4.450647 4.454594 4.454720 4.455385 4.455431 4.456888 4.460278
#> [197] 4.461328 4.461499 4.461659 4.465495 4.465783 4.466074 4.469590
#> [204] 4.472388 4.474185 4.475486 4.477030 4.479476 4.489424 4.491337
#> [211] 4.497600 4.499194 4.501830 4.506042 4.509599 4.510136 4.511997
#> [218] 4.513574 4.515757 4.516190 4.517871 4.520576 4.526393 4.527307
#> [225] 4.531136 4.532010 4.535109 4.535235 4.535362 4.539148 4.545288
#> [232] 4.548208 4.550722 4.551098 4.552190 4.557715 4.558985 4.559113
#> [239] 4.562158 4.563726 4.564559 4.564785 4.568252 4.569945 4.570329
#> [246] 4.571776 4.579925 4.580394 4.582285 4.590286 4.592607 4.594835
#> [253] 4.596705 4.598000 4.598871 4.600291 4.602280 4.603179 4.606045
#> [260] 4.610318 4.610648 4.611733 4.613242 4.614636 4.615299 4.616702
#> [267] 4.616946 4.619780 4.623169 4.623742 4.624599 4.624719 4.625456
#> [274] 4.627881 4.628745 4.629218 4.629726 4.633007 4.636055 4.642022
#> [281] 4.648447 4.649889 4.652298 4.653326 4.653754 4.656019 4.657910
#> [288] 4.658260 4.659263 4.659548 4.659866 4.661207 4.662169 4.662864
#> [295] 4.664120 4.664762 4.664841 4.667338 4.670696 4.674144 4.674237
#> [302] 4.679176 4.679704 4.679973 4.681125 4.681692 4.682104 4.682850
#> [309] 4.683049 4.685088 4.688885 4.689728 4.691052 4.693350 4.696641
#> [316] 4.697520 4.702357 4.703799 4.706959 4.707108 4.708236 4.708596
#> [323] 4.713647 4.716789 4.727227 4.730815 4.738587 4.739919 4.740427
#> [330] 4.741619 4.742243 4.742613 4.744205 4.746566 4.747887 4.748156
#> [337] 4.749302 4.750839 4.751103 4.751692 4.756472 4.758022 4.760124
#> [344] 4.761899 4.765853 4.772694 4.773114 4.777049 4.777371 4.780346
#> [351] 4.784943 4.788002 4.789382 4.793611 4.794100 4.794329 4.796908
#> [358] 4.797589 4.797787 4.797851 4.799006 4.801819 4.802157 4.802619
#> [365] 4.807421 4.807609 4.808099 4.809225 4.809879 4.810145 4.811039
#> [372] 4.816127 4.816136 4.818380 4.818443 4.818457 4.819663 4.821506
#> [379] 4.822948 4.824905 4.826361 4.826866 4.827997 4.828654 4.828983
#> [386] 4.829228 4.838700 4.839209 4.841633 4.843996 4.845621 4.849431
#> [393] 4.849768 4.852057 4.852392 4.854387 4.860249 4.861147 4.861279
#> [400] 4.865572 4.866503 4.869356 4.870738 4.870881 4.872573 4.873811
#> [407] 4.874122 4.876288 4.876597 4.877438 4.878028 4.882408 4.883814
#> [414] 4.884378 4.885682 4.886443 4.886504 4.888556 4.890750 4.890959
#> [421] 4.894034 4.894123 4.896674 4.897063 4.897257 4.901628 4.903557
#> [428] 4.904322 4.905144 4.906490 4.911409 4.911666 4.913845 4.915731
#> [435] 4.916366 4.918209 4.919784 4.921180 4.921583 4.927150 4.933301
#> [442] 4.933584 4.935528 4.935556 4.937133 4.937287 4.939657 4.941129
#> [449] 4.941728 4.942205 4.944828 4.945295 4.947072 4.947836 4.948660
#> [456] 4.949219 4.950247 4.951689 4.951871 4.953047 4.954143 4.955868
#> [463] 4.957600 4.957810 4.958304 4.960976 4.961637 4.963966 4.965452
#> [470] 4.966080 4.967572 4.969876 4.970377 4.974637 4.977271 4.978258
#> [477] 4.979991 4.982078 4.982852 4.984522 4.985198 4.987780 4.989056
#> [484] 4.990435 4.990997 4.992731 4.993720 4.993855 4.995569 4.998511
#> [491] 4.998547 5.002637 5.003149 5.003733 5.003943 5.005530 5.006228
#> [498] 5.008610 5.008631 5.010148 5.010753 5.012366 5.013330 5.013639
#> [505] 5.015437 5.015530 5.019417 5.019793 5.020920 5.022175 5.022441
#> [512] 5.022651 5.026094 5.026546 5.029140 5.029842 5.031149 5.032935
#> [519] 5.033037 5.033305 5.033937 5.034018 5.035511 5.035673 5.038367
#> [526] 5.042373 5.045136 5.045504 5.045645 5.047283 5.047638 5.048657
#> [533] 5.049548 5.050296 5.051687 5.055639 5.056394 5.058420 5.058541
#> [540] 5.060045 5.060093 5.060446 5.061408 5.062121 5.063489 5.063675
#> [547] 5.065036 5.066417 5.067536 5.067731 5.067914 5.067932 5.068798
#> [554] 5.070262 5.071109 5.071707 5.071931 5.073218 5.073225 5.074499
#> [561] 5.074967 5.075733 5.077461 5.077487 5.079814 5.082135 5.084095
#> [568] 5.085873 5.086342 5.087391 5.087948 5.089399 5.090387 5.091367
#> [575] 5.093070 5.093159 5.094100 5.095073 5.098520 5.098800 5.099684
#> [582] 5.100171 5.100228 5.104960 5.107722 5.110296 5.111514 5.113251
#> [589] 5.114032 5.117215 5.117966 5.118291 5.118953 5.121449 5.126131
#> [596] 5.127427 5.129092 5.129124 5.129271 5.135248 5.135618 5.136453
#> [603] 5.136798 5.136905 5.142294 5.143530 5.144002 5.145110 5.145737
#> [610] 5.145782 5.146523 5.150678 5.154189 5.155632 5.156444 5.156450
#> [617] 5.157687 5.158981 5.161004 5.163614 5.164062 5.165675 5.168256
#> [624] 5.168467 5.169337 5.169947 5.176031 5.178155 5.179928 5.180344
#> [631] 5.181737 5.187409 5.187889 5.187897 5.188848 5.189964 5.195452
#> [638] 5.199189 5.200465 5.200929 5.207057 5.210444 5.215236 5.216211
#> [645] 5.217161 5.219773 5.221550 5.223249 5.226219 5.227899 5.230002
#> [652] 5.231825 5.234433 5.235566 5.236813 5.243919 5.246905 5.246984
#> [659] 5.247726 5.250586 5.254230 5.254714 5.255669 5.255924 5.256048
#> [666] 5.260777 5.261634 5.262286 5.265271 5.265364 5.267058 5.268578
#> [673] 5.268715 5.270987 5.271719 5.273154 5.274882 5.276154 5.277490
#> [680] 5.277540 5.281136 5.281552 5.284024 5.286290 5.287147 5.289564
#> [687] 5.294180 5.294564 5.296088 5.299078 5.299281 5.300562 5.301988
#> [694] 5.303634 5.304953 5.305533 5.307549 5.308039 5.308215 5.312601
#> [701] 5.313295 5.315128 5.316437 5.317792 5.318651 5.318748 5.322125
#> [708] 5.322251 5.322833 5.326457 5.327368 5.328738 5.330262 5.331277
#> [715] 5.331681 5.334774 5.336367 5.337161 5.339528 5.339641 5.343005
#> [722] 5.348794 5.351279 5.351572 5.353373 5.357416 5.359153 5.361931
#> [729] 5.363724 5.368643 5.371366 5.372745 5.374075 5.378957 5.380451
#> [736] 5.382568 5.383985 5.386483 5.386769 5.389111 5.391350 5.392283
#> [743] 5.393130 5.393532 5.394609 5.399582 5.399715 5.401700 5.404705
#> [750] 5.410907 5.411271 5.411361 5.411764 5.413438 5.413850 5.414216
#> [757] 5.414345 5.414417 5.414899 5.415986 5.417235 5.417615 5.418753
#> [764] 5.423094 5.423946 5.426663 5.427235 5.429080 5.429274 5.430217
#> [771] 5.431508 5.436378 5.436679 5.439868 5.439904 5.442746 5.445337
#> [778] 5.448015 5.451237 5.451391 5.452722 5.454193 5.455212 5.456333
#> [785] 5.456852 5.457504 5.460073 5.460582 5.461035 5.464107 5.472032
#> [792] 5.473087 5.473130 5.473369 5.477180 5.478262 5.480734 5.481699
#> [799] 5.485290 5.486989 5.487034 5.489468 5.490311 5.493862 5.494154
#> [806] 5.494403 5.495753 5.498800 5.499231 5.499938 5.501977 5.504883
#> [813] 5.505348 5.506943 5.507303 5.508615 5.510536 5.515858 5.518802
#> [820] 5.519865 5.530003 5.530090 5.530973 5.534888 5.536977 5.542958
#> [827] 5.543382 5.548210 5.549942 5.550576 5.555026 5.557985 5.558899
#> [834] 5.559251 5.561673 5.563814 5.565254 5.566564 5.567732 5.570850
#> [841] 5.571259 5.571443 5.572601 5.573089 5.575017 5.581186 5.583850
#> [848] 5.589142 5.590179 5.591698 5.591864 5.594252 5.594937 5.595773
#> [855] 5.596577 5.596632 5.598547 5.599382 5.601964 5.602715 5.606315
#> [862] 5.607392 5.608490 5.609995 5.611064 5.611847 5.613574 5.614026
#> [869] 5.615580 5.617079 5.617704 5.618550 5.620138 5.622767 5.624443
#> [876] 5.627008 5.631640 5.639663 5.640270 5.641012 5.641433 5.642631
#> [883] 5.645315 5.652018 5.653055 5.659804 5.661085 5.663149 5.665814
#> [890] 5.666595 5.667602 5.670241 5.671516 5.672647 5.673465 5.675957
#> [897] 5.676814 5.677598 5.679912 5.682577 5.682603 5.682851 5.689855
#> [904] 5.690326 5.691455 5.693165 5.693597 5.694212 5.695802 5.696524
#> [911] 5.697221 5.698368 5.699171 5.704037 5.705319 5.705443 5.705953
#> [918] 5.706450 5.710804 5.711708 5.712236 5.712527 5.714056 5.715807
#> [925] 5.716017 5.717116 5.717404 5.718410 5.719711 5.720504 5.721099
#> [932] 5.723513 5.724210 5.726231 5.726897 5.728151 5.729407 5.730013
#> [939] 5.730201 5.734772 5.738698 5.741793 5.744223 5.745812 5.746220
#> [946] 5.747474 5.749218 5.750917 5.752106 5.752299 5.753626 5.754772
#> [953] 5.756701 5.760917 5.762501 5.765739 5.766826 5.767228 5.767378
#> [960] 5.768004 5.770489 5.771153 5.773159 5.774144 5.777538 5.777723
#> [967] 5.778889 5.780757 5.783123 5.783278 5.785026 5.791028 5.792243
#> [974] 5.795360 5.796718 5.797112 5.797367 5.798758 5.801220 5.801965
#> [981] 5.804833 5.806046 5.807935 5.808171 5.809918 5.810248 5.810855
#> [988] 5.812171 5.812673 5.812845 5.814827 5.816434 5.819524 5.819952
#> [995] 5.820638 5.827460 5.830982 5.832360 5.835048 5.835373 5.836885
#> [1002] 5.836920 5.837160 5.837407 5.837520 5.838791 5.841054 5.841108
#> [1009] 5.841123 5.844155 5.845769 5.848083 5.849301 5.853338 5.855472
#> [1016] 5.857036 5.857576 5.860630 5.860894 5.868212 5.868552 5.869299
#> [1023] 5.869762 5.869899 5.876530 5.878141 5.878891 5.880136 5.881585
#> [1030] 5.885845 5.887106 5.887157 5.887887 5.888371 5.888813 5.889399
#> [1037] 5.889442 5.890186 5.890908 5.890994 5.891494 5.891886 5.892009
#> [1044] 5.892541 5.892990 5.894333 5.895605 5.895711 5.896341 5.897089
#> [1051] 5.898446 5.900169 5.904375 5.906166 5.906266 5.908676 5.910352
#> [1058] 5.911232 5.911880 5.915004 5.915198 5.916363 5.917019 5.917744
#> [1065] 5.920003 5.921183 5.923377 5.924506 5.924609 5.926306 5.929543
#> [1072] 5.930396 5.930544 5.930730 5.936229 5.939440 5.940049 5.943094
#> [1079] 5.943562 5.945342 5.946637 5.946778 5.948162 5.948387 5.948850
#> [1086] 5.949485 5.952791 5.953255 5.958603 5.960401 5.962445 5.963141
#> [1093] 5.963913 5.963939 5.964945 5.966572 5.969190 5.969456 5.971532
#> [1100] 5.973176 5.974835 5.976651 5.976678 5.978564 5.978686 5.979802
#> [1107] 5.982229 5.983575 5.984402 5.985241 5.986529 5.986828 5.987286
#> [1114] 5.988816 5.990800 5.991488 5.991668 5.993280 5.994778 5.995858
#> [1121] 5.996173 5.996894 5.998541 5.999273 5.999749 6.000316 6.000683
#> [1128] 6.002070 6.002974 6.003697 6.004939 6.005089 6.007521 6.009389
#> [1135] 6.009805 6.009905 6.010267 6.011360 6.011480 6.013667 6.013815
#> [1142] 6.015047 6.015570 6.018139 6.019862 6.021156 6.021446 6.022891
#> [1149] 6.024850 6.026813 6.027366 6.027926 6.028098 6.033516 6.036137
#> [1156] 6.036423 6.037479 6.038026 6.038713 6.041906 6.044592 6.048471
#> [1163] 6.049976 6.052257 6.052895 6.053431 6.060360 6.061023 6.063694
#> [1170] 6.064002 6.066262 6.067760 6.068665 6.069285 6.069308 6.069544
#> [1177] 6.070933 6.072370 6.073565 6.073956 6.074131 6.074272 6.075572
#> [1184] 6.077366 6.078971 6.081289 6.082976 6.085542 6.089669 6.090763
#> [1191] 6.090905 6.091777 6.092230 6.093967 6.095300 6.095550 6.096699
#> [1198] 6.097441 6.099595 6.102215 6.102528 6.103856 6.107648 6.107785
#> [1205] 6.108607 6.109690 6.110367 6.111166 6.111896 6.112983 6.114180
#> [1212] 6.114933 6.115752 6.116329 6.116742 6.117027 6.119735 6.119837
#> [1219] 6.120508 6.121333 6.121414 6.123997 6.124766 6.125810 6.126186
#> [1226] 6.126229 6.126381 6.127150 6.127500 6.127992 6.130125 6.131109
#> [1233] 6.131161 6.132392 6.132667 6.133738 6.134476 6.135193 6.135637
#> [1240] 6.135852 6.136106 6.137562 6.139073 6.140927 6.144809 6.145085
#> [1247] 6.146022 6.146424 6.146477 6.147291 6.149210 6.150610 6.151023
#> [1254] 6.151807 6.153386 6.153591 6.153714 6.154435 6.155209 6.155335
#> [1261] 6.156005 6.156132 6.158905 6.158906 6.160147 6.160443 6.160746
#> [1268] 6.160758 6.162627 6.164896 6.165036 6.166529 6.168427 6.168787
#> [1275] 6.170653 6.171834 6.171972 6.172163 6.172687 6.174691 6.182020
#> [1282] 6.182372 6.182778 6.185467 6.185721 6.186479 6.186706 6.187916
#> [1289] 6.190440 6.191139 6.192104 6.192608 6.192645 6.193485 6.194785
#> [1296] 6.195986 6.197603 6.197711 6.198424 6.199453 6.199866 6.202178
#> [1303] 6.202822 6.204601 6.205502 6.207626 6.207710 6.210309 6.212716
#> [1310] 6.213832 6.214057 6.214428 6.215680 6.217749 6.231209 6.231847
#> [1317] 6.233350 6.233891 6.245624 6.246075 6.246192 6.247458 6.249233
#> [1324] 6.249629 6.251263 6.252907 6.254701 6.256027 6.256578 6.258843
#> [1331] 6.259224 6.259402 6.259860 6.260212 6.261230 6.262694 6.263152
#> [1338] 6.264690 6.264897 6.265204 6.266847 6.268336 6.268384 6.273526
#> [1345] 6.274201 6.275483 6.276874 6.277062 6.280899 6.281283 6.283429
#> [1352] 6.283978 6.287638 6.290700 6.292484 6.293771 6.295752 6.297017
#> [1359] 6.297148 6.300585 6.301394 6.301653 6.302081 6.302221 6.302587
#> [1366] 6.303882 6.304069 6.304519 6.305752 6.305832 6.307802 6.308232
#> [1373] 6.308446 6.309993 6.311543 6.312216 6.313103 6.315886 6.316274
#> [1380] 6.317898 6.318203 6.320136 6.322520 6.322802 6.325462 6.327612
#> [1387] 6.327898 6.329858 6.331962 6.332026 6.332499 6.332675 6.334339
#> [1394] 6.334480 6.334490 6.337262 6.338270 6.338439 6.339072 6.339407
#> [1401] 6.339876 6.342721 6.343276 6.344070 6.349693 6.351007 6.351569
#> [1408] 6.351890 6.352252 6.352465 6.354615 6.356928 6.356945 6.357724
#> [1415] 6.357868 6.357958 6.359429 6.364362 6.364767 6.364823 6.366251
#> [1422] 6.369137 6.370618 6.371607 6.372056 6.372289 6.373075 6.373528
#> [1429] 6.375979 6.378183 6.379352 6.379443 6.380764 6.382856 6.384225
#> [1436] 6.384666 6.385434 6.385797 6.387736 6.393811 6.393926 6.395063
#> [1443] 6.395970 6.398478 6.400103 6.403574 6.404089 6.404120 6.405373
#> [1450] 6.406724 6.406865 6.407238 6.407594 6.408187 6.408456 6.409238
#> [1457] 6.409952 6.410185 6.410550 6.410665 6.411076 6.411172 6.413823
#> [1464] 6.415113 6.416141 6.416608 6.417020 6.417266 6.417372 6.417427
#> [1471] 6.417478 6.417503 6.418444 6.418703 6.420941 6.422152 6.423143
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#> [1485] 6.432403 6.433790 6.435753 6.436103 6.436550 6.436789 6.437915
#> [1492] 6.438594 6.438632 6.438922 6.439289 6.439368 6.440225 6.444253
#> [1499] 6.444306 6.448311 6.449595 6.450707 6.451582 6.452478 6.453504
#> [1506] 6.453506 6.455028 6.455487 6.456474 6.456820 6.457419 6.457528
#> [1513] 6.457655 6.458038 6.464113 6.464132 6.466211 6.467654 6.468113
#> [1520] 6.468567 6.468608 6.470221 6.476710 6.477295 6.477715 6.480218
#> [1527] 6.481098 6.484308 6.484678 6.484982 6.487278 6.489201 6.489296
#> [1534] 6.489372 6.489565 6.490274 6.492757 6.492810 6.498848 6.499422
#> [1541] 6.500195 6.503692 6.504893 6.505198 6.505964 6.506003 6.506529
#> [1548] 6.507507 6.510317 6.510840 6.510909 6.513266 6.513364 6.513513
#> [1555] 6.515276 6.516397 6.519515 6.521350 6.521460 6.522155 6.523247
#> [1562] 6.523834 6.524462 6.524850 6.529837 6.535040 6.535944 6.536695
#> [1569] 6.537813 6.539875 6.540478 6.540570 6.542596 6.543564 6.543578
#> [1576] 6.544954 6.545041 6.545653 6.546008 6.546400 6.552684 6.552847
#> [1583] 6.553938 6.554682 6.555714 6.555994 6.558390 6.558837 6.558993
#> [1590] 6.563165 6.564752 6.566312 6.566338 6.571453 6.572206 6.574914
#> [1597] 6.575534 6.576743 6.577611 6.577665 6.578326 6.578501 6.579071
#> [1604] 6.579500 6.580424 6.580905 6.586762 6.587501 6.588930 6.589015
#> [1611] 6.589180 6.590409 6.592033 6.592881 6.595714 6.596331 6.597219
#> [1618] 6.599064 6.602086 6.605143 6.605962 6.606520 6.606924 6.610958
#> [1625] 6.611138 6.611442 6.612448 6.616620 6.616750 6.616863 6.616937
#> [1632] 6.617424 6.618023 6.619967 6.620443 6.621943 6.622728 6.626179
#> [1639] 6.626210 6.626952 6.629479 6.629768 6.630107 6.630984 6.632145
#> [1646] 6.632627 6.635000 6.637152 6.637451 6.637692 6.638523 6.639009
#> [1653] 6.639110 6.640406 6.641918 6.642920 6.642982 6.643717 6.647377
#> [1660] 6.648898 6.650080 6.651973 6.652941 6.653483 6.655153 6.655363
#> [1667] 6.655759 6.655891 6.659892 6.660906 6.661238 6.661303 6.663285
#> [1674] 6.663386 6.665982 6.666120 6.666124 6.667306 6.671828 6.672165
#> [1681] 6.674324 6.674754 6.675793 6.676349 6.683213 6.686034 6.686996
#> [1688] 6.687197 6.690218 6.692649 6.692917 6.695391 6.697828 6.701499
#> [1695] 6.702052 6.702285 6.703720 6.704316 6.704427 6.704471 6.704547
#> [1702] 6.705232 6.706994 6.708382 6.712039 6.714230 6.718328 6.718469
#> [1709] 6.719164 6.719700 6.723325 6.724998 6.726837 6.728148 6.728254
#> [1716] 6.728259 6.730650 6.733731 6.733891 6.734101 6.734394 6.734423
#> [1723] 6.735197 6.735559 6.736317 6.738045 6.738634 6.740670 6.740981
#> [1730] 6.742012 6.743486 6.744083 6.744385 6.744852 6.745073 6.746460
#> [1737] 6.746864 6.747184 6.747890 6.748295 6.748641 6.749410 6.751269
#> [1744] 6.752146 6.752388 6.753726 6.754533 6.754867 6.755127 6.756304
#> [1751] 6.756410 6.760190 6.763630 6.764674 6.764850 6.767937 6.768255
#> [1758] 6.770282 6.771778 6.775987 6.777364 6.777821 6.779987 6.780158
#> [1765] 6.781734 6.782207 6.783389 6.783589 6.786464 6.788209 6.789391
#> [1772] 6.790153 6.790907 6.792021 6.792198 6.793345 6.793560 6.794409
#> [1779] 6.794492 6.796542 6.797789 6.798141 6.798187 6.798320 6.799073
#> [1786] 6.800642 6.801268 6.801529 6.801831 6.803039 6.803177 6.803403
#> [1793] 6.804984 6.806819 6.806938 6.807514 6.812205 6.812434 6.814649
#> [1800] 6.817882 6.818442 6.821235 6.822359 6.823004 6.828066 6.828204
#> [1807] 6.830703 6.830761 6.831105 6.832827 6.834746 6.836166 6.837851
#> [1814] 6.842074 6.845862 6.851820 6.853402 6.855119 6.858102 6.859114
#> [1821] 6.860374 6.860420 6.861689 6.862853 6.863419 6.865933 6.866409
#> [1828] 6.868402 6.868771 6.870012 6.872545 6.872556 6.873087 6.877382
#> [1835] 6.877740 6.878436 6.878489 6.878966 6.879300 6.879707 6.884423
#> [1842] 6.884438 6.884725 6.886150 6.886364 6.890347 6.891070 6.891570
#> [1849] 6.892450 6.893573 6.895074 6.897002 6.899908 6.905284 6.906092
#> [1856] 6.911406 6.911534 6.914028 6.916000 6.917246 6.917761 6.918889
#> [1863] 6.919200 6.920221 6.922799 6.924845 6.924960 6.926153 6.928931
#> [1870] 6.931368 6.931452 6.931976 6.932678 6.933942 6.937644 6.938505
#> [1877] 6.938991 6.940276 6.946567 6.948700 6.949386 6.950622 6.951291
#> [1884] 6.953299 6.953600 6.954710 6.956544 6.957518 6.958011 6.959459
#> [1891] 6.960108 6.960585 6.961947 6.964950 6.967000 6.967337 6.968639
#> [1898] 6.969792 6.972832 6.975478 6.976183 6.979264 6.981365 6.983374
#> [1905] 6.984246 6.984895 6.986898 6.987198 6.987787 6.990986 6.995474
#> [1912] 6.995481 6.996006 6.996317 6.997208 7.002831 7.003145 7.004928
#> [1919] 7.005953 7.006073 7.006561 7.007248 7.007534 7.008497 7.009960
#> [1926] 7.010326 7.010338 7.010405 7.011337 7.013827 7.015499 7.015834
#> [1933] 7.018231 7.018351 7.019208 7.022678 7.022726 7.022932 7.023682
#> [1940] 7.027664 7.030758 7.033125 7.034165 7.034770 7.035062 7.038103
#> [1947] 7.039128 7.041061 7.041190 7.041553 7.045038 7.046327 7.047804
#> [1954] 7.048823 7.051275 7.052446 7.053666 7.055801 7.055877 7.056861
#> [1961] 7.057234 7.058488 7.060075 7.060758 7.061179 7.062578 7.062818
#> [1968] 7.063263 7.063958 7.065172 7.066753 7.070311 7.070991 7.075772
#> [1975] 7.076079 7.076506 7.077032 7.077952 7.079672 7.079768 7.082922
#> [1982] 7.084627 7.084778 7.085793 7.085793 7.086592 7.087843 7.088502
#> [1989] 7.089076 7.092359 7.092456 7.093776 7.094291 7.095784 7.095787
#> [1996] 7.097786 7.098083 7.101612 7.101735 7.102999 7.103345 7.106011
#> [2003] 7.106682 7.106747 7.108578 7.110430 7.112132 7.114345 7.118812
#> [2010] 7.119021 7.119617 7.120064 7.120153 7.122223 7.122541 7.122616
#> [2017] 7.125860 7.126030 7.126615 7.128305 7.132336 7.133020 7.133459
#> [2024] 7.133841 7.134625 7.134729 7.135583 7.135730 7.138982 7.140463
#> [2031] 7.141034 7.143214 7.143476 7.145005 7.146416 7.149567 7.149884
#> [2038] 7.150005 7.151894 7.153104 7.153297 7.153569 7.155355 7.155865
#> [2045] 7.157555 7.158687 7.162725 7.162921 7.163691 7.164043 7.167348
#> [2052] 7.169000 7.170976 7.171029 7.171292 7.175071 7.175281 7.177028
#> [2059] 7.178868 7.181715 7.181770 7.183211 7.185654 7.188098 7.189766
#> [2066] 7.190935 7.194403 7.199596 7.200718 7.201177 7.202879 7.203440
#> [2073] 7.206385 7.208260 7.209327 7.209841 7.212959 7.216625 7.217808
#> [2080] 7.219432 7.219947 7.220681 7.221314 7.222695 7.223204 7.223410
#> [2087] 7.225037 7.225585 7.225617 7.229116 7.229563 7.230498 7.230723
#> [2094] 7.231196 7.231521 7.232023 7.234436 7.235186 7.235197 7.236094
#> [2101] 7.236333 7.237220 7.238407 7.238538 7.239168 7.241282 7.242267
#> [2108] 7.244709 7.245120 7.245142 7.246563 7.247235 7.247776 7.247919
#> [2115] 7.248177 7.250122 7.253465 7.253688 7.253847 7.255956 7.258252
#> [2122] 7.258487 7.259732 7.260571 7.262137 7.262275 7.263376 7.264370
#> [2129] 7.268829 7.271002 7.271797 7.272354 7.273411 7.273590 7.277101
#> [2136] 7.278966 7.279615 7.279804 7.282843 7.285031 7.288036 7.291441
#> [2143] 7.291935 7.292780 7.293055 7.294488 7.295552 7.296659 7.297561
#> [2150] 7.299951 7.302293 7.303078 7.307274 7.308513 7.308989 7.309605
#> [2157] 7.311444 7.311536 7.312188 7.312585 7.312951 7.313697 7.313810
#> [2164] 7.315658 7.317474 7.317536 7.318760 7.318956 7.320715 7.320719
#> [2171] 7.324406 7.326539 7.327958 7.328712 7.330668 7.333970 7.335711
#> [2178] 7.336702 7.336863 7.337302 7.338826 7.338919 7.339141 7.340087
#> [2185] 7.341549 7.341574 7.347067 7.347900 7.350297 7.351873 7.353057
#> [2192] 7.353246 7.354227 7.354866 7.355914 7.356202 7.356396 7.356771
#> [2199] 7.359758 7.361071 7.361132 7.362216 7.362972 7.364073 7.364450
#> [2206] 7.364696 7.368046 7.368954 7.369723 7.371143 7.372979 7.373107
#> [2213] 7.373251 7.373909 7.375700 7.376214 7.376818 7.378398 7.378729
#> [2220] 7.381149 7.381189 7.382547 7.387276 7.389198 7.391708 7.392869
#> [2227] 7.393390 7.394284 7.395562 7.396564 7.397263 7.397590 7.398420
#> [2234] 7.399247 7.402232 7.402648 7.403462 7.404589 7.407028 7.407741
#> [2241] 7.410226 7.411820 7.413107 7.416266 7.416279 7.416750 7.417937
#> [2248] 7.418487 7.419285 7.420686 7.421381 7.421542 7.423670 7.426252
#> [2255] 7.426454 7.427531 7.427732 7.431891 7.431970 7.435643 7.435782
#> [2262] 7.437303 7.437591 7.438424 7.441065 7.443325 7.443886 7.444254
#> [2269] 7.445392 7.445794 7.447461 7.447679 7.448009 7.448127 7.449005
#> [2276] 7.450717 7.451852 7.453799 7.454748 7.456573 7.457313 7.458447
#> [2283] 7.459109 7.459356 7.460636 7.461431 7.462831 7.465206 7.465414
#> [2290] 7.466447 7.466664 7.468819 7.468955 7.470871 7.472214 7.472479
#> [2297] 7.474991 7.475831 7.476721 7.476905 7.477755 7.478224 7.481319
#> [2304] 7.482504 7.482888 7.482924 7.484300 7.485791 7.486409 7.487027
#> [2311] 7.487862 7.491651 7.497690 7.499640 7.501456 7.503775 7.504931
#> [2318] 7.506477 7.509622 7.511246 7.512588 7.514777 7.515653 7.516006
#> [2325] 7.516846 7.517285 7.517399 7.518932 7.519253 7.520347 7.521515
#> [2332] 7.522871 7.523004 7.524359 7.525097 7.525309 7.525640 7.526009
#> [2339] 7.528424 7.529705 7.530548 7.530554 7.530558 7.531459 7.531526
#> [2346] 7.535866 7.536091 7.536187 7.536189 7.539206 7.542466 7.542669
#> [2353] 7.542928 7.544596 7.546775 7.547709 7.551289 7.551348 7.555670
#> [2360] 7.557144 7.562500 7.564037 7.566671 7.566718 7.566789 7.567780
#> [2367] 7.568144 7.568162 7.570676 7.574837 7.576331 7.576569 7.579522
#> [2374] 7.581979 7.582950 7.583285 7.585407 7.586043 7.586306 7.587998
#> [2381] 7.590629 7.593833 7.596858 7.597494 7.598225 7.600238 7.600430
#> [2388] 7.600984 7.602269 7.602674 7.608132 7.611390 7.612964 7.613729
#> [2395] 7.614354 7.615674 7.617716 7.618595 7.619484 7.620725 7.622284
#> [2402] 7.622768 7.624570 7.624655 7.627137 7.627813 7.628368 7.632064
#> [2409] 7.632382 7.636333 7.636532 7.637109 7.637885 7.641257 7.641477
#> [2416] 7.645876 7.647939 7.648739 7.649559 7.650813 7.652867 7.659098
#> [2423] 7.660381 7.662102 7.664338 7.664653 7.665024 7.667476 7.669062
#> [2430] 7.669537 7.674215 7.674450 7.674604 7.674960 7.675408 7.675604
#> [2437] 7.676952 7.681285 7.682201 7.687331 7.691273 7.691460 7.692666
#> [2444] 7.692903 7.693644 7.694836 7.695371 7.697904 7.699045 7.699156
#> [2451] 7.699214 7.702024 7.703188 7.703443 7.704562 7.705085 7.705438
#> [2458] 7.708902 7.709036 7.710457 7.710609 7.712220 7.712775 7.714431
#> [2465] 7.715994 7.721903 7.723566 7.723627 7.725373 7.728230 7.729712
#> [2472] 7.733797 7.734875 7.735180 7.737081 7.737219 7.737420 7.738669
#> [2479] 7.739661 7.740087 7.740945 7.741337 7.743318 7.744926 7.745885
#> [2486] 7.746120 7.747360 7.747489 7.749288 7.750458 7.752392 7.755308
#> [2493] 7.756067 7.759521 7.760212 7.761615 7.761695 7.762207 7.762631
#> [2500] 7.764685 7.766316 7.767130 7.769093 7.769833 7.771335 7.772323
#> [2507] 7.772923 7.773054 7.774012 7.774724 7.774736 7.774775 7.776582
#> [2514] 7.779661 7.780040 7.780838 7.782129 7.783962 7.785405 7.787185
#> [2521] 7.787856 7.788432 7.788617 7.791043 7.792232 7.793174 7.793566
#> [2528] 7.795449 7.796513 7.797186 7.797609 7.797692 7.798954 7.800797
#> [2535] 7.802814 7.803642 7.807654 7.808991 7.814496 7.815516 7.815530
#> [2542] 7.817645 7.818104 7.822342 7.822539 7.826798 7.828425 7.829655
#> [2549] 7.830116 7.830188 7.830208 7.830889 7.831053 7.831417 7.832557
#> [2556] 7.833294 7.833667 7.834415 7.837135 7.838066 7.838365 7.838580
#> [2563] 7.838617 7.839098 7.839363 7.841273 7.841682 7.842811 7.842957
#> [2570] 7.843024 7.845321 7.846027 7.846622 7.847541 7.847919 7.851112
#> [2577] 7.852452 7.854008 7.854880 7.855500 7.859807 7.864640 7.867354
#> [2584] 7.867552 7.867620 7.868050 7.869011 7.871224 7.872242 7.874936
#> [2591] 7.875129 7.875676 7.875984 7.876012 7.877526 7.878604 7.881056
#> [2598] 7.881407 7.881810 7.883832 7.884351 7.884375 7.884828 7.886335
#> [2605] 7.887583 7.889802 7.890744 7.892756 7.893703 7.893770 7.893773
#> [2612] 7.894641 7.894771 7.895282 7.899872 7.900492 7.900981 7.903079
#> [2619] 7.903217 7.903403 7.903984 7.905301 7.906173 7.907971 7.908341
#> [2626] 7.909946 7.911943 7.912526 7.913344 7.914179 7.914503 7.916771
#> [2633] 7.917069 7.917911 7.919806 7.919848 7.921838 7.924387 7.924847
#> [2640] 7.925061 7.925114 7.928497 7.928571 7.928834 7.929562 7.929727
#> [2647] 7.931059 7.935187 7.935824 7.943101 7.943197 7.944780 7.946403
#> [2654] 7.948374 7.949428 7.950575 7.950673 7.950994 7.951116 7.953692
#> [2661] 7.958392 7.959128 7.960160 7.960356 7.962697 7.963585 7.964639
#> [2668] 7.964908 7.964984 7.965779 7.967156 7.967198 7.967222 7.967394
#> [2675] 7.967589 7.969022 7.970819 7.971932 7.971996 7.972910 7.973016
#> [2682] 7.974022 7.974299 7.974503 7.975235 7.975724 7.976318 7.978133
#> [2689] 7.979603 7.980496 7.982575 7.982598 7.984348 7.984701 7.990604
#> [2696] 7.993543 7.996173 7.996371 7.997274 7.998066 7.999105 8.000102
#> [2703] 8.001750 8.002279 8.002523 8.002563 8.002586 8.004266 8.006923
#> [2710] 8.007004 8.007347 8.008758 8.009411 8.009894 8.011502 8.012779
#> [2717] 8.014083 8.015398 8.015884 8.017832 8.018560 8.020524 8.022909
#> [2724] 8.023541 8.025651 8.027966 8.028270 8.029054 8.029698 8.029895
#> [2731] 8.030797 8.030875 8.032524 8.038013 8.040643 8.042372 8.044598
#> [2738] 8.044688 8.044912 8.046381 8.047493 8.048321 8.050595 8.050702
#> [2745] 8.050768 8.051574 8.055831 8.057011 8.057018 8.057212 8.063402
#> [2752] 8.064034 8.064459 8.067689 8.068246 8.068543 8.069837 8.072324
#> [2759] 8.072360 8.072729 8.073360 8.074199 8.074509 8.077607 8.078183
#> [2766] 8.079284 8.082689 8.083272 8.083625 8.085719 8.088031 8.089496
#> [2773] 8.090638 8.091825 8.093483 8.093778 8.093785 8.095422 8.098609
#> [2780] 8.098899 8.099355 8.099477 8.100974 8.104181 8.105818 8.105996
#> [2787] 8.106722 8.107240 8.110570 8.110817 8.111020 8.114458 8.120330
#> [2794] 8.120928 8.121098 8.123026 8.124399 8.124416 8.126411 8.126703
#> [2801] 8.130208 8.133003 8.133056 8.133527 8.137331 8.140496 8.140635
#> [2808] 8.141830 8.142487 8.145790 8.146561 8.147103 8.147568 8.147606
#> [2815] 8.150360 8.150865 8.152188 8.153179 8.153690 8.154858 8.159229
#> [2822] 8.159308 8.160054 8.161155 8.161498 8.161776 8.162556 8.166517
#> [2829] 8.171709 8.172030 8.172938 8.173642 8.174510 8.175324 8.176604
#> [2836] 8.177016 8.183799 8.185786 8.185806 8.187812 8.188416 8.188765
#> [2843] 8.189450 8.190721 8.190741 8.191816 8.194783 8.195033 8.195710
#> [2850] 8.197676 8.202381 8.204429 8.205159 8.205566 8.210040 8.210611
#> [2857] 8.213175 8.214535 8.218677 8.221542 8.221665 8.221867 8.222484
#> [2864] 8.223110 8.224998 8.225325 8.225906 8.226719 8.228041 8.230984
#> [2871] 8.232050 8.232428 8.232758 8.234012 8.236784 8.238565 8.238656
#> [2878] 8.241973 8.242150 8.242234 8.243271 8.243284 8.245447 8.246119
#> [2885] 8.248662 8.249664 8.251537 8.252090 8.252899 8.254059 8.254186
#> [2892] 8.254647 8.255741 8.256822 8.257079 8.257611 8.258585 8.258834
#> [2899] 8.259214 8.260076 8.260663 8.261818 8.261850 8.267563 8.268919
#> [2906] 8.269330 8.270511 8.271003 8.274362 8.280541 8.281507 8.281710
#> [2913] 8.281810 8.283615 8.284982 8.285394 8.285620 8.285741 8.286148
#> [2920] 8.288329 8.288898 8.289036 8.292157 8.292860 8.293491 8.294123
#> [2927] 8.294247 8.294591 8.296081 8.296252 8.297475 8.299062 8.304754
#> [2934] 8.304968 8.305114 8.305691 8.305862 8.307533 8.307881 8.308118
#> [2941] 8.310585 8.311560 8.314317 8.314411 8.314615 8.319256 8.319457
#> [2948] 8.320103 8.321937 8.322905 8.323560 8.326452 8.327138 8.329397
#> [2955] 8.330572 8.331233 8.331692 8.337152 8.337781 8.341127 8.342693
#> [2962] 8.343873 8.344110 8.345335 8.346034 8.346115 8.346446 8.346533
#> [2969] 8.349885 8.350065 8.351563 8.352119 8.352316 8.353304 8.354777
#> [2976] 8.355203 8.355917 8.356819 8.358703 8.359806 8.361533 8.361629
#> [2983] 8.362788 8.363806 8.364617 8.364675 8.365164 8.365392 8.366189
#> [2990] 8.366414 8.367794 8.368291 8.369813 8.370391 8.370911 8.372180
#> [2997] 8.374438 8.376086 8.376301 8.377015 8.377427 8.378017 8.378783
#> [3004] 8.378854 8.380606 8.381209 8.382110 8.382167 8.383877 8.384764
#> [3011] 8.384915 8.388038 8.388526 8.390704 8.391039 8.392230 8.392392
#> [3018] 8.395524 8.395597 8.396173 8.397603 8.398216 8.399201 8.399441
#> [3025] 8.403799 8.404182 8.404920 8.405061 8.407258 8.408539 8.408688
#> [3032] 8.410041 8.410441 8.410452 8.410931 8.411356 8.411515 8.412618
#> [3039] 8.412789 8.413189 8.414980 8.415889 8.417470 8.418347 8.419716
#> [3046] 8.420388 8.420903 8.421302 8.430020 8.430643 8.431232 8.432108
#> [3053] 8.433215 8.434888 8.438797 8.439159 8.439363 8.442936 8.444227
#> [3060] 8.444599 8.447272 8.449052 8.449253 8.450608 8.451080 8.451551
#> [3067] 8.453401 8.456088 8.456437 8.456511 8.458529 8.460417 8.461071
#> [3074] 8.462285 8.464230 8.464565 8.465294 8.466411 8.469333 8.469405
#> [3081] 8.469456 8.469630 8.472007 8.474013 8.475671 8.476403 8.477404
#> [3088] 8.478379 8.480103 8.480529 8.483438 8.483448 8.483647 8.483670
#> [3095] 8.483974 8.484448 8.486424 8.487793 8.488842 8.488956 8.489456
#> [3102] 8.491357 8.495149 8.495619 8.497995 8.499302 8.499553 8.499639
#> [3109] 8.502697 8.502994 8.503925 8.505549 8.505596 8.505996 8.508183
#> [3116] 8.508987 8.509635 8.511996 8.513549 8.514753 8.515163 8.515381
#> [3123] 8.515526 8.518752 8.518835 8.519120 8.521195 8.524338 8.525565
#> [3130] 8.526967 8.527424 8.528124 8.531231 8.535659 8.535794 8.536881
#> [3137] 8.536885 8.537584 8.537882 8.538587 8.540247 8.540838 8.541565
#> [3144] 8.542861 8.546278 8.547034 8.547081 8.547582 8.548621 8.549350
#> [3151] 8.549648 8.551092 8.552877 8.553488 8.554158 8.554471 8.556209
#> [3158] 8.557786 8.557798 8.557925 8.558011 8.558484 8.558559 8.560059
#> [3165] 8.560261 8.561483 8.562394 8.562704 8.565174 8.565563 8.565628
#> [3172] 8.566706 8.568807 8.570381 8.571021 8.571812 8.571832 8.572584
#> [3179] 8.573092 8.573195 8.573245 8.573493 8.574331 8.574591 8.577601
#> [3186] 8.577668 8.577685 8.577855 8.579986 8.580185 8.580251 8.580463
#> [3193] 8.586314 8.586909 8.593003 8.594664 8.595466 8.597682 8.600319
#> [3200] 8.600479 8.600621 8.600654 8.604135 8.605011 8.607607 8.607787
#> [3207] 8.608993 8.609291 8.614759 8.615620 8.615763 8.616705 8.617068
#> [3214] 8.619193 8.623103 8.623855 8.626360 8.627023 8.628262 8.629912
#> [3221] 8.630966 8.631219 8.634299 8.634369 8.635591 8.638182 8.638473
#> [3228] 8.638536 8.640290 8.640452 8.641385 8.642428 8.642507 8.642706
#> [3235] 8.643069 8.644537 8.646092 8.646370 8.646495 8.648554 8.649496
#> [3242] 8.649607 8.649841 8.649850 8.650858 8.651747 8.652761 8.654762
#> [3249] 8.655367 8.657417 8.657631 8.659405 8.663915 8.664308 8.664515
#> [3256] 8.665373 8.666549 8.667892 8.667984 8.671368 8.672399 8.672994
#> [3263] 8.673543 8.674977 8.675913 8.677326 8.677934 8.681280 8.684010
#> [3270] 8.685577 8.686873 8.688035 8.689430 8.695054 8.695958 8.696343
#> [3277] 8.697722 8.698999 8.700097 8.700663 8.702019 8.703054 8.703185
#> [3284] 8.704809 8.706725 8.707572 8.708525 8.708951 8.714177 8.714239
#> [3291] 8.714867 8.716150 8.716197 8.717727 8.717736 8.718221 8.718589
#> [3298] 8.719203 8.722772 8.724778 8.725430 8.726674 8.727055 8.727135
#> [3305] 8.731137 8.732133 8.733331 8.734715 8.735905 8.737137 8.737482
#> [3312] 8.739629 8.740163 8.741869 8.745603 8.745843 8.749573 8.752365
#> [3319] 8.752500 8.753787 8.755337 8.756931 8.758390 8.759222 8.761163
#> [3326] 8.761188 8.762260 8.762437 8.762726 8.763348 8.764490 8.764552
#> [3333] 8.765525 8.765730 8.766465 8.767593 8.767790 8.770180 8.770244
#> [3340] 8.770598 8.774326 8.775925 8.776569 8.778973 8.780801 8.782419
#> [3347] 8.786079 8.787333 8.791696 8.793309 8.793429 8.794463 8.795028
#> [3354] 8.795984 8.797624 8.799233 8.799809 8.802672 8.803777 8.804106
#> [3361] 8.805103 8.806032 8.807529 8.810031 8.810233 8.810242 8.811069
#> [3368] 8.813006 8.814423 8.815255 8.815758 8.816518 8.817205 8.819498
#> [3375] 8.820216 8.820598 8.820713 8.820746 8.821109 8.821375 8.825062
#> [3382] 8.825303 8.825680 8.832249 8.833171 8.834022 8.836274 8.836685
#> [3389] 8.836978 8.841646 8.843636 8.844065 8.844150 8.847317 8.849496
#> [3396] 8.850010 8.850222 8.851757 8.852444 8.852847 8.854262 8.856271
#> [3403] 8.856955 8.861581 8.862421 8.863112 8.863855 8.864636 8.867690
#> [3410] 8.869260 8.870457 8.871408 8.872979 8.873438 8.875371 8.877155
#> [3417] 8.878340 8.878737 8.879731 8.882172 8.882663 8.883668 8.884798
#> [3424] 8.886105 8.887280 8.888318 8.888648 8.888941 8.893834 8.896265
#> [3431] 8.898281 8.900388 8.900916 8.904182 8.908547 8.915683 8.919841
#> [3438] 8.923474 8.923844 8.924533 8.924940 8.926548 8.930034 8.931958
#> [3445] 8.937552 8.941860 8.943352 8.943396 8.946001 8.946683 8.947462
#> [3452] 8.949589 8.951629 8.952262 8.952335 8.953249 8.955437 8.955797
#> [3459] 8.957368 8.957949 8.958302 8.961175 8.970080 8.970108 8.970276
#> [3466] 8.974415 8.983986 8.984545 8.985750 8.988224 8.989206 8.989219
#> [3473] 8.991785 8.992736 8.993147 8.995433 9.002659 9.003054 9.003636
#> [3480] 9.006449 9.008897 9.008902 9.009903 9.011104 9.015312 9.015520
#> [3487] 9.016158 9.019929 9.020110 9.022283 9.022895 9.023195 9.027230
#> [3494] 9.031576 9.031730 9.032474 9.035081 9.035740 9.036773 9.037017
#> [3501] 9.037304 9.042693 9.044150 9.046509 9.047737 9.050493 9.050832
#> [3508] 9.051192 9.053302 9.053913 9.054575 9.057451 9.061361 9.064714
#> [3515] 9.065148 9.066041 9.068375 9.074405 9.075669 9.076006 9.076143
#> [3522] 9.078214 9.079503 9.080767 9.081851 9.084375 9.084590 9.085076
#> [3529] 9.085630 9.085901 9.086042 9.088595 9.088646 9.089769 9.090782
#> [3536] 9.090981 9.091303 9.099982 9.101872 9.103041 9.103619 9.106312
#> [3543] 9.107413 9.107943 9.109522 9.109584 9.111964 9.112204 9.114620
#> [3550] 9.120171 9.121716 9.122027 9.123374 9.125955 9.132075 9.134542
#> [3557] 9.135826 9.140252 9.140708 9.141059 9.141786 9.143407 9.144639
#> [3564] 9.145149 9.145925 9.147977 9.149036 9.149815 9.152521 9.155406
#> [3571] 9.155503 9.155930 9.156509 9.157353 9.158127 9.158651 9.159788
#> [3578] 9.174155 9.174698 9.175742 9.175914 9.175973 9.180946 9.181850
#> [3585] 9.184785 9.185663 9.186573 9.186587 9.186761 9.186794 9.188197
#> [3592] 9.188405 9.188822 9.190471 9.192968 9.193743 9.195802 9.204529
#> [3599] 9.204860 9.206427 9.209966 9.212557 9.213688 9.214400 9.215439
#> [3606] 9.215881 9.216503 9.217755 9.218139 9.219222 9.219829 9.221271
#> [3613] 9.221621 9.222446 9.222762 9.225454 9.226756 9.227178 9.228148
#> [3620] 9.228869 9.230845 9.230868 9.233018 9.236073 9.238282 9.240927
#> [3627] 9.243637 9.244637 9.246072 9.253709 9.254371 9.254581 9.258080
#> [3634] 9.260508 9.261150 9.261999 9.263007 9.267789 9.268144 9.269299
#> [3641] 9.271805 9.272659 9.275337 9.281812 9.285040 9.285257 9.287395
#> [3648] 9.287865 9.289848 9.291128 9.291516 9.292653 9.293878 9.294325
#> [3655] 9.295309 9.295713 9.296655 9.297656 9.298053 9.298813 9.300018
#> [3662] 9.300610 9.302747 9.303820 9.304673 9.309464 9.310791 9.312245
#> [3669] 9.312581 9.312588 9.313103 9.314107 9.314405 9.314589 9.314957
#> [3676] 9.317256 9.320029 9.320766 9.321198 9.321818 9.321856 9.322144
#> [3683] 9.322265 9.323903 9.324477 9.325622 9.325926 9.326371 9.327389
#> [3690] 9.330845 9.331785 9.331895 9.333492 9.334570 9.334822 9.336140
#> [3697] 9.341289 9.342343 9.343300 9.343734 9.348817 9.349110 9.352292
#> [3704] 9.353996 9.354336 9.357516 9.361277 9.361361 9.362671 9.363259
#> [3711] 9.364064 9.364735 9.365678 9.366556 9.370540 9.371928 9.372025
#> [3718] 9.373396 9.373613 9.376577 9.378428 9.380409 9.381142 9.381234
#> [3725] 9.384668 9.387942 9.388886 9.389450 9.390229 9.390540 9.391662
#> [3732] 9.392277 9.392742 9.396761 9.397202 9.397323 9.397668 9.400325
#> [3739] 9.406802 9.408146 9.408915 9.409259 9.410775 9.412158 9.414436
#> [3746] 9.414821 9.420492 9.420755 9.421826 9.424519 9.426052 9.426695
#> [3753] 9.428575 9.429291 9.429373 9.429374 9.430623 9.430789 9.430890
#> [3760] 9.432203 9.432651 9.434996 9.436371 9.436928 9.439803 9.440049
#> [3767] 9.440255 9.441321 9.445570 9.446372 9.446651 9.447448 9.447465
#> [3774] 9.449339 9.449573 9.449597 9.453425 9.453513 9.454971 9.458894
#> [3781] 9.458982 9.459810 9.461972 9.464336 9.464805 9.466562 9.466937
#> [3788] 9.469168 9.470208 9.470410 9.471623 9.472435 9.473734 9.476060
#> [3795] 9.477849 9.478234 9.483317 9.483912 9.484836 9.485093 9.485433
#> [3802] 9.485572 9.486332 9.487009 9.487904 9.488974 9.489057 9.489830
#> [3809] 9.491934 9.492541 9.492634 9.495400 9.495491 9.497733 9.499843
#> [3816] 9.500535 9.501174 9.506811 9.507161 9.508806 9.510406 9.512897
#> [3823] 9.513667 9.514616 9.516033 9.516244 9.516480 9.517482 9.518332
#> [3830] 9.518514 9.519121 9.520094 9.520456 9.521218 9.523923 9.525528
#> [3837] 9.534346 9.534600 9.535020 9.537259 9.541345 9.542546 9.544813
#> [3844] 9.546763 9.548075 9.549080 9.552191 9.555348 9.557330 9.558069
#> [3851] 9.560925 9.561150 9.562563 9.565394 9.568940 9.569484 9.570250
#> [3858] 9.570420 9.571342 9.571555 9.571584 9.573573 9.574801 9.575229
#> [3865] 9.577259 9.577335 9.581586 9.581684 9.582244 9.585314 9.586953
#> [3872] 9.587542 9.587848 9.589407 9.590288 9.591366 9.593398 9.595110
#> [3879] 9.595595 9.595685 9.599107 9.600303 9.608915 9.609321 9.610641
#> [3886] 9.610709 9.614140 9.616552 9.618359 9.620054 9.620913 9.621161
#> [3893] 9.623375 9.624184 9.626365 9.634049 9.634159 9.634234 9.638376
#> [3900] 9.638395 9.639133 9.640636 9.641518 9.642906 9.645571 9.646513
#> [3907] 9.649587 9.649732 9.652829 9.652845 9.654143 9.654495 9.655809
#> [3914] 9.656032 9.658705 9.659885 9.660509 9.660511 9.662146 9.665183
#> [3921] 9.665271 9.668573 9.669084 9.670286 9.670806 9.671098 9.673256
#> [3928] 9.675572 9.675952 9.677698 9.683466 9.685297 9.685448 9.686310
#> [3935] 9.686718 9.688027 9.689472 9.696702 9.696838 9.696983 9.699138
#> [3942] 9.700247 9.700509 9.700569 9.703647 9.704324 9.704439 9.707522
#> [3949] 9.709853 9.717100 9.717584 9.718397 9.719034 9.721194 9.721953
#> [3956] 9.722471 9.722669 9.722774 9.724518 9.728739 9.729734 9.732728
#> [3963] 9.737673 9.739158 9.740126 9.740624 9.741477 9.741595 9.743265
#> [3970] 9.747558 9.750839 9.752240 9.757845 9.761180 9.761705 9.763720
#> [3977] 9.763847 9.764686 9.764725 9.765398 9.766316 9.766450 9.771312
#> [3984] 9.771720 9.773106 9.776433 9.781286 9.782635 9.783106 9.786348
#> [3991] 9.786732 9.788369 9.789234 9.790020 9.790611 9.793480 9.794162
#> [3998] 9.794205 9.794700 9.794727 9.795160 9.796989 9.797282 9.797609
#> [4005] 9.798715 9.801757 9.804817 9.808595 9.810494 9.818309 9.819101
#> [4012] 9.821873 9.823581 9.825989 9.829298 9.831014 9.832290 9.833455
#> [4019] 9.834392 9.837582 9.841691 9.842949 9.843483 9.844245 9.844813
#> [4026] 9.846940 9.847433 9.854821 9.857773 9.859233 9.859487 9.861457
#> [4033] 9.861605 9.862718 9.864258 9.864814 9.865488 9.865756 9.867142
#> [4040] 9.867281 9.867863 9.869772 9.876686 9.876875 9.879065 9.879252
#> [4047] 9.884195 9.884931 9.885292 9.886390 9.886517 9.886554 9.887498
#> [4054] 9.889626 9.892015 9.892021 9.893091 9.895845 9.895959 9.896076
#> [4061] 9.896126 9.896489 9.896550 9.896766 9.897440 9.900939 9.902861
#> [4068] 9.903911 9.905088 9.905409 9.908163 9.909160 9.910565 9.913359
#> [4075] 9.914466 9.914964 9.915522 9.917984 9.920934 9.922470 9.925470
#> [4082] 9.927353 9.927414 9.929131 9.930129 9.934324 9.934526 9.935728
#> [4089] 9.936634 9.937818 9.939009 9.940074 9.941928 9.942549 9.944577
#> [4096] 9.945062 9.945080 9.945274 9.945781 9.947245 9.949459 9.952128
#> [4103] 9.952464 9.955527 9.955993 9.956648 9.957646 9.958233 9.961530
#> [4110] 9.962317 9.965240 9.966780 9.966856 9.968311 9.973190 9.977606
#> [4117] 9.980170 9.980726 9.983060 9.984908 9.986207 9.986622 9.987680
#> [4124] 9.989137 9.992896 9.996611 9.998073 10.000884 10.004164 10.004634
#> [4131] 10.007823 10.008387 10.008943 10.013104 10.015731 10.016402 10.018278
#> [4138] 10.019494 10.019503 10.019725 10.022882 10.024502 10.026482 10.026629
#> [4145] 10.027764 10.031237 10.031846 10.038397 10.040598 10.041132 10.041446
#> [4152] 10.042059 10.045100 10.048084 10.049684 10.050547 10.050604 10.054014
#> [4159] 10.054790 10.056502 10.063867 10.066082 10.066538 10.069066 10.069575
#> [4166] 10.070766 10.075620 10.079324 10.079445 10.080117 10.080604 10.082525
#> [4173] 10.088502 10.089334 10.090379 10.090905 10.091077 10.091600 10.095070
#> [4180] 10.096798 10.100674 10.100960 10.106545 10.107400 10.107513 10.108085
#> [4187] 10.109763 10.109853 10.111141 10.111290 10.111368 10.112595 10.116553
#> [4194] 10.117074 10.120724 10.124675 10.127163 10.127291 10.131782 10.132193
#> [4201] 10.132786 10.132869 10.134803 10.135584 10.136912 10.141553 10.143504
#> [4208] 10.144404 10.144456 10.146208 10.146712 10.146845 10.148902 10.150412
#> [4215] 10.155713 10.155958 10.157291 10.158934 10.162107 10.162285 10.162531
#> [4222] 10.162564 10.162889 10.164653 10.165365 10.166084 10.171475 10.173028
#> [4229] 10.176299 10.176362 10.176995 10.180530 10.184593 10.184951 10.185708
#> [4236] 10.187192 10.187979 10.190323 10.192176 10.192908 10.192959 10.196476
#> [4243] 10.197806 10.198232 10.201398 10.203462 10.203669 10.204453 10.204796
#> [4250] 10.207113 10.207936 10.208762 10.210085 10.211436 10.214481 10.219401
#> [4257] 10.220739 10.221674 10.221765 10.222109 10.222197 10.222318 10.223024
#> [4264] 10.224526 10.225542 10.228179 10.230273 10.230823 10.232226 10.232907
#> [4271] 10.233098 10.235690 10.237528 10.237925 10.240612 10.241694 10.242800
#> [4278] 10.244584 10.244884 10.249220 10.249772 10.249937 10.250309 10.251054
#> [4285] 10.251522 10.255726 10.260333 10.263361 10.269291 10.269975 10.270951
#> [4292] 10.276895 10.279789 10.280784 10.281672 10.283641 10.283909 10.284127
#> [4299] 10.284751 10.285105 10.287056 10.291751 10.293007 10.293812 10.298558
#> [4306] 10.303768 10.307935 10.312362 10.314914 10.317203 10.320898 10.324576
#> [4313] 10.325476 10.327385 10.330246 10.330262 10.332120 10.333343 10.336858
#> [4320] 10.341417 10.341590 10.344363 10.347846 10.355420 10.356025 10.357556
#> [4327] 10.362456 10.364045 10.367549 10.368136 10.369482 10.371357 10.374423
#> [4334] 10.375222 10.376242 10.378141 10.381923 10.382724 10.383153 10.384083
#> [4341] 10.384352 10.386378 10.391211 10.394937 10.395672 10.397083 10.397728
#> [4348] 10.399558 10.402394 10.403258 10.404789 10.405144 10.406025 10.407916
#> [4355] 10.410774 10.414729 10.414924 10.415185 10.415318 10.416874 10.417976
#> [4362] 10.419501 10.425726 10.427290 10.427660 10.428629 10.431103 10.432289
#> [4369] 10.433672 10.436330 10.437326 10.438353 10.444884 10.447140 10.447272
#> [4376] 10.448525 10.449892 10.455451 10.458696 10.459783 10.462910 10.468541
#> [4383] 10.469876 10.470240 10.475037 10.477249 10.477452 10.478034 10.478317
#> [4390] 10.481385 10.484525 10.491484 10.492459 10.493018 10.493376 10.498064
#> [4397] 10.500983 10.501044 10.501285 10.505555 10.507180 10.516235 10.517132
#> [4404] 10.518353 10.523631 10.523979 10.524375 10.527955 10.531645 10.534872
#> [4411] 10.535054 10.544044 10.545746 10.548092 10.548634 10.551585 10.552932
#> [4418] 10.553082 10.554587 10.556869 10.560018 10.560960 10.562467 10.563401
#> [4425] 10.569298 10.571302 10.572470 10.572569 10.572768 10.574042 10.574522
#> [4432] 10.574636 10.575741 10.575993 10.580610 10.585398 10.585657 10.588714
#> [4439] 10.592012 10.593111 10.595003 10.595607 10.598058 10.599102 10.599524
#> [4446] 10.601823 10.601984 10.603215 10.604018 10.604196 10.606533 10.608438
#> [4453] 10.609010 10.610491 10.614700 10.615684 10.630307 10.631637 10.632163
#> [4460] 10.638278 10.638715 10.638790 10.641503 10.642095 10.642235 10.643572
#> [4467] 10.644370 10.644574 10.644775 10.646449 10.649705 10.650332 10.654778
#> [4474] 10.661154 10.661312 10.668591 10.673036 10.673409 10.681741 10.684090
#> [4481] 10.685256 10.685910 10.687804 10.688441 10.692770 10.693170 10.693647
#> [4488] 10.694030 10.695664 10.696020 10.698758 10.700633 10.702145 10.712433
#> [4495] 10.713218 10.713655 10.714032 10.717596 10.724695 10.726557 10.727254
#> [4502] 10.728913 10.730083 10.731027 10.733447 10.733471 10.734225 10.737952
#> [4509] 10.741223 10.741268 10.743106 10.751672 10.757872 10.762733 10.765391
#> [4516] 10.766567 10.766680 10.767409 10.767476 10.768797 10.771638 10.776333
#> [4523] 10.776910 10.791785 10.793564 10.797483 10.797649 10.800085 10.803558
#> [4530] 10.804445 10.804838 10.808039 10.808744 10.812165 10.813331 10.813851
#> [4537] 10.814717 10.816605 10.821305 10.823322 10.825681 10.825773 10.828262
#> [4544] 10.830279 10.832476 10.840337 10.840340 10.840540 10.852360 10.853332
#> [4551] 10.857976 10.860118 10.860954 10.863007 10.866012 10.874330 10.879452
#> [4558] 10.880245 10.887583 10.889606 10.891984 10.897419 10.897672 10.899984
#> [4565] 10.900763 10.905584 10.907401 10.909003 10.911645 10.914645 10.920928
#> [4572] 10.925485 10.926159 10.928881 10.932188 10.932479 10.937636 10.938301
#> [4579] 10.948056 10.952561 10.955747 10.960300 10.960536 10.962454 10.962580
#> [4586] 10.963077 10.964117 10.969500 10.970873 10.972402 10.975648 10.978596
#> [4593] 10.985464 10.985508 10.988169 10.988304 10.992957 10.993006 11.000821
#> [4600] 11.001841 11.003264 11.003503 11.004303 11.009755 11.014622 11.023400
#> [4607] 11.024375 11.024554 11.030463 11.033112 11.035707 11.044663 11.049632
#> [4614] 11.053596 11.054015 11.054089 11.054861 11.057728 11.058018 11.058818
#> [4621] 11.060698 11.065443 11.068971 11.071009 11.072483 11.072818 11.075401
#> [4628] 11.082383 11.084847 11.087050 11.094761 11.095546 11.095667 11.098824
#> [4635] 11.099283 11.099964 11.100976 11.102845 11.103455 11.106501 11.106831
#> [4642] 11.106851 11.107601 11.109487 11.111734 11.115263 11.118707 11.121720
#> [4649] 11.122407 11.122735 11.128384 11.131557 11.132546 11.135017 11.135332
#> [4656] 11.136177 11.141350 11.142155 11.143966 11.146961 11.148066 11.152397
#> [4663] 11.153658 11.157528 11.158065 11.161764 11.164834 11.167194 11.173251
#> [4670] 11.175174 11.179371 11.180095 11.181399 11.182991 11.183554 11.189275
#> [4677] 11.193884 11.194092 11.195626 11.197423 11.198279 11.199424 11.202019
#> [4684] 11.204588 11.207981 11.210251 11.210371 11.213055 11.213740 11.214478
#> [4691] 11.215437 11.220444 11.224201 11.229991 11.232291 11.233463 11.234189
#> [4698] 11.235060 11.236578 11.237798 11.244899 11.245332 11.248048 11.249513
#> [4705] 11.249650 11.250606 11.252140 11.252930 11.254307 11.256234 11.258154
#> [4712] 11.259252 11.259276 11.259392 11.260206 11.261225 11.262112 11.263768
#> [4719] 11.265245 11.265965 11.267890 11.268455 11.269374 11.271658 11.271705
#> [4726] 11.273073 11.274257 11.274345 11.282481 11.288624 11.290663 11.291686
#> [4733] 11.292637 11.293501 11.296100 11.306308 11.313883 11.315218 11.317123
#> [4740] 11.326229 11.326252 11.326495 11.330247 11.332265 11.333135 11.333432
#> [4747] 11.333598 11.335816 11.337512 11.337919 11.338055 11.340523 11.344938
#> [4754] 11.346257 11.347432 11.350838 11.351737 11.353317 11.354236 11.355357
#> [4761] 11.356198 11.357309 11.361382 11.364164 11.368430 11.371124 11.372453
#> [4768] 11.376129 11.377528 11.378825 11.382727 11.386310 11.386466 11.386903
#> [4775] 11.387116 11.388904 11.394540 11.395463 11.396353 11.398600 11.399192
#> [4782] 11.402169 11.402412 11.403226 11.404895 11.405120 11.407780 11.408732
#> [4789] 11.409533 11.410065 11.416271 11.417853 11.420374 11.422534 11.426762
#> [4796] 11.429990 11.431338 11.432672 11.434715 11.440788 11.443983 11.444796
#> [4803] 11.446752 11.449387 11.449864 11.449880 11.454593 11.460433 11.461945
#> [4810] 11.462980 11.464821 11.478187 11.478544 11.478888 11.489012 11.490258
#> [4817] 11.495273 11.502033 11.502631 11.503940 11.505592 11.506812 11.512732
#> [4824] 11.521508 11.523560 11.531601 11.535678 11.536750 11.540296 11.540586
#> [4831] 11.543325 11.543781 11.546915 11.549051 11.552789 11.558649 11.566802
#> [4838] 11.567034 11.567344 11.568933 11.575628 11.576656 11.580205 11.580622
#> [4845] 11.581053 11.595122 11.597205 11.605111 11.613353 11.613586 11.617135
#> [4852] 11.618361 11.618777 11.619224 11.620507 11.620684 11.625745 11.636571
#> [4859] 11.636632 11.637819 11.639811 11.640743 11.642030 11.644287 11.649058
#> [4866] 11.650634 11.650885 11.653271 11.655908 11.656478 11.656703 11.657229
#> [4873] 11.657292 11.658640 11.658700 11.662408 11.663364 11.666234 11.668382
#> [4880] 11.668818 11.669530 11.673042 11.673114 11.675516 11.676508 11.680729
#> [4887] 11.681915 11.696152 11.698924 11.703277 11.704386 11.712358 11.717466
#> [4894] 11.718600 11.721511 11.723937 11.731437 11.735747 11.736142 11.749755
#> [4901] 11.763708 11.763778 11.772307 11.779107 11.780986 11.787617 11.788605
#> [4908] 11.792700 11.793968 11.795504 11.796928 11.802907 11.807211 11.811595
#> [4915] 11.813759 11.821091 11.823886 11.824989 11.835796 11.835805 11.837457
#> [4922] 11.841592 11.851130 11.852639 11.854108 11.854585 11.859100 11.864000
#> [4929] 11.867746 11.877533 11.884597 11.885114 11.886833 11.889094 11.891431
#> [4936] 11.895489 11.902273 11.903858 11.905648 11.910699 11.920040 11.920274
#> [4943] 11.921295 11.924813 11.925464 11.926249 11.942631 11.952506 11.959204
#> [4950] 11.963607 11.964684 11.967591 11.970569 11.971724 11.981849 11.987708
#> [4957] 11.996250 11.998521 11.998673 12.000562 12.007095 12.008240 12.008948
#> [4964] 12.015084 12.020168 12.023551 12.024463 12.026011 12.032219 12.046767
#> [4971] 12.062090 12.063196 12.084123 12.088924 12.090712 12.097666 12.105407
#> [4978] 12.105812 12.106752 12.107408 12.112645 12.116316 12.120333 12.124373
#> [4985] 12.128035 12.129417 12.130550 12.136857 12.137435 12.156677 12.160788
#> [4992] 12.167520 12.168157 12.171006 12.180026 12.183592 12.187182 12.189354
#> [4999] 12.202196 12.213360 12.223888 12.225296 12.228537 12.228606 12.236943
#> [5006] 12.247168 12.247737 12.272190 12.272376 12.276988 12.285025 12.291347
#> [5013] 12.293458 12.293655 12.309013 12.334602 12.341750 12.352574 12.356021
#> [5020] 12.393153 12.393390 12.402313 12.403783 12.404562 12.405113 12.405795
#> [5027] 12.414676 12.433099 12.437083 12.440618 12.442219 12.453473 12.462773
#> [5034] 12.469288 12.472445 12.481818 12.489611 12.495034 12.497743 12.498739
#> [5041] 12.508491 12.508747 12.510235 12.511340 12.515303 12.520220 12.526220
#> [5048] 12.534910 12.539402 12.541175 12.541945 12.543384 12.561268 12.562384
#> [5055] 12.578259 12.593170 12.613505 12.633984 12.646982 12.647404 12.652614
#> [5062] 12.659668 12.661784 12.665864 12.669630 12.676252 12.679478 12.681168
#> [5069] 12.684195 12.693420 12.732826 12.767965 12.774912 12.788354 12.795722
#> [5076] 12.827656 12.834318 12.834416 12.863790 12.867119 12.884097 12.886725
#> [5083] 12.889825 12.923374 12.930357 12.938205 12.998100 13.002538 13.002655
#> [5090] 13.003397 13.032034 13.032538 13.037352 13.049455 13.060079 13.068273
#> [5097] 13.121465 13.130443 13.159924 13.165906 13.167057 13.186829 13.200693
#> [5104] 13.201307 13.203640 13.274295 13.283580 13.393983 13.432739 13.454298
#> [5111] 13.467261 13.487412 13.671700 13.764203 13.766897 13.769740 13.779365
#> [5118] 13.799265 13.806023 13.818302 13.879100 13.919596 13.939101 14.010936
#> [5125] 14.059320 14.067351 14.082262 14.128692 14.173357 14.236301 14.248875
#> [5132] 14.269512
dat <- dat %>% mutate(temp = ifelse(quarter == 1, NA, temp))
#> mutate: changed 5,144 values (60%) of 'temp' (5144 new NA)
sort(unique(filter(dat, quarter == 1)$temp))
#> filter: removed 3,452 rows (40%), 5,144 rows remaining
#> numeric(0)Add back in the data that get environmental variables added
fishdat2 <- fishdat %>% filter(!year %in% unique(dat$year))
#> filter: removed 8,597 rows (96%), 342 rows remaining
sort(unique(fishdat2$year))
#> [1] 1992 2020
sort(unique(dat$year))
#> [1] 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
#> [16] 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
dat <- bind_rows(dat, fishdat2)
sort(unique(dat$year))
#> [1] 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
#> [16] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
dat %>% group_by(year, quarter) %>% summarise(mean_temp = mean(temp)) %>% as.data.frame()
#> group_by: 2 grouping variables (year, quarter)
#> summarise: now 58 rows and 3 columns, one group variable remaining (year)
#> year quarter mean_temp
#> 1 1992 1 NA
#> 2 1992 4 NA
#> 3 1993 1 NA
#> 4 1993 4 7.246987
#> 5 1994 1 NA
#> 6 1994 4 8.091562
#> 7 1995 1 NA
#> 8 1995 4 8.407659
#> 9 1996 1 NA
#> 10 1996 4 8.299413
#> 11 1997 1 NA
#> 12 1997 4 8.528001
#> 13 1998 1 NA
#> 14 1998 4 7.360535
#> 15 1999 1 NA
#> 16 1999 4 8.453743
#> 17 2000 1 NA
#> 18 2000 4 8.082708
#> 19 2001 1 NA
#> 20 2001 4 8.077808
#> 21 2002 1 NA
#> 22 2002 4 8.748811
#> 23 2003 1 NA
#> 24 2003 4 8.485861
#> 25 2004 1 NA
#> 26 2004 4 8.506533
#> 27 2005 1 NA
#> 28 2005 4 7.659136
#> 29 2006 1 NA
#> 30 2006 4 8.112038
#> 31 2007 1 NA
#> 32 2007 4 8.120095
#> 33 2008 1 NA
#> 34 2008 4 8.407919
#> 35 2009 1 NA
#> 36 2009 4 8.943984
#> 37 2010 1 NA
#> 38 2010 4 6.407201
#> 39 2011 1 NA
#> 40 2011 4 7.330912
#> 41 2012 1 NA
#> 42 2012 4 7.976262
#> 43 2013 1 NA
#> 44 2013 4 7.473096
#> 45 2014 1 NA
#> 46 2014 4 9.228403
#> 47 2015 1 NA
#> 48 2015 4 8.406676
#> 49 2016 1 NA
#> 50 2016 4 9.294175
#> 51 2017 1 NA
#> 52 2017 4 8.372061
#> 53 2018 1 NA
#> 54 2018 4 9.580076
#> 55 2019 1 NA
#> 56 2019 4 8.319438
#> 57 2020 1 NA
#> 58 2020 4 NAAdd UTM coords
# First add UTM coords
# Add UTM coords
# Function
LongLatToUTM <- function(x, y, zone){
xy <- data.frame(ID = 1:length(x), X = x, Y = y)
coordinates(xy) <- c("X", "Y")
proj4string(xy) <- CRS("+proj=longlat +datum=WGS84") ## for example
res <- spTransform(xy, CRS(paste("+proj=utm +zone=",zone," ellps=WGS84",sep='')))
return(as.data.frame(res))
}
utm_coords <- LongLatToUTM(dat$lon, dat$lat, zone = 33)
#> Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
#> prefer_proj): Discarded datum Unknown based on WGS84 ellipsoid in CRS definition
dat$X <- utm_coords$X/1000 # for computational reasons
dat$Y <- utm_coords$Y/1000 # for computational reasonsMake Gifs
Save data
write.csv(dat, file = "data/for_analysis/catch_q_1_4.csv", row.names = FALSE)